ZA200610644B - Oligonucleotides for cancer diagnosis - Google Patents

Description

OLIGONUCLEOTIDES FOR CANCER DIAGNOSIS

The present invention relates to oligonucleotide probes, for use in assessing gene transcript levels in a cell, which may be used in analytical techniques, particularly diagnostic techniques. Conveniently the probes are provided in kit form. Different sets of probes may be used in techniques to prepare gene expression patterns and identify, diagnose or monitor different cancers or stages thereof.

The identification of quick and easy methods of sample analysis for, for example, diagnostic applications, remains the goal of many researchers. End users seek methods which are cost effective, produce statistically significant results and which may be implemented routinely without the need for highly skilled individuals.

The analysis of gene expression within cells has been used to provide information on the state of those cells and importantly the state of the individual from which the cells are derived. The relative expression of various genes in a cell has been identified as reflecting a particular state within a body. For example, cancer cells are known to exhibit altered expression of various proteins and the transcripts or the expressed proteins may therefore be used as markers of that disease state.

Thus biopsy tissue may be analysed for the presence of these markers and cells originating from the site of the disease may be identified in other tissues or fluids of the body by the presence of the markers. Furthermore, products of the altered expression may be released into the blood stream and these products may be analysed. In addition cells which have contacted disease cells may be affected by their direct contact with those cells resulting in altered gene expression and their expression or products of expression may be similarly analysed.

However, there are some limitations with these methods. For example, the use of specific tumour markers for identifying cancer suffers from a variety’ of defects, such as lack of specificity or sensitivity, association of the marker with disease states besides the specific type of cancer, and difficulty of detection in asymptomatic individuals.

In addition to the analysis of one or two marker transcripts or proteins, more recently, gene expression patterns have been analysed. Most of the work involving large-scale gene expression analysis with implications in disease diagnosis has involved clinical samples originating from diseased tissues or cells. For example, several recent publications, which demonstrate that gene expression data can be used to distinguish between similar cancer types, have used clinical samples from diseased tissues or cells (Alon et al. 1999, PNAS, 96, p6745-6750; Golub et al. 1999, Science, 286, p531 -537,; Alizadeh et al, 2000,

Nature, 403, p503-511; Bittner et al., 2000, Nature, 406, p536-540).

However, these methods have relied on analysis of a sample containing diseased cells or products of those cells or cells which have been contacted by disease cells. Analysis of such samples relies on knowledge of the presence ofa disease and its location, which may be difficult in asymptomatic patients.

Furthermore, samples can not always be taken from the disease site, e.g. in diseases of the brain.

In a finding of great significance, the present inventors identified the previously untapped potential of all cells within a body to provide information relating to the state of the organism from which the cells were derived.

W098/49342 describes the analysis of the gene expression of cells distant from the site of disease, e.g. peripheral blood collected distant from a cancer site.

PCT/GB03/005102, incorporated herein by reference, describes specific probes for the diagnosis of breast cancer and Alzheimer's disease and discusses protocols for identifying other appropriate probes for that purpose and for diagnosing other diseases.

This finding is based on the premise that the different parts of an organism's body exist in dynamic interaction with each other. When a disease affects one part of the body, other parts of the body are also affected. The interaction results from a wide spectrum of biochemical signals that are released from the diseased area, affecting other areas in the body. Although, the nature of the biochemical and physiological changes induced by the released signals can vary in the different body parts, the changes can be measured at the level of gene expression and used for diagnostic purposes.

The physiological state of a cell in an organism is determined by the pattern with which genes are expressed in it. The pattern depends upon the internal and external biological stimuli to which said cell is exposed, and any change either in the extent or in the nature of these stimuli can lead to a change in the pattern with which the different genes are expressed in the cell. There is a growing understanding that by analysing the systemic changes in gene expression patterns in cells in biological samples, it is possible to provide information on the type and nature of the biological stimuli that are acting on them. Thus, for example, by monitoring the expression of a large number of genes in cells in a test sample, it is possible to determine whether their genes are expressed with a pattern characteristic for a particular disease, condition or stage thereof. Measuring changes in gene activities in cells, e.g. from tissue or body fluids is therefore emerging as a powerful tool for disease diagnosis.

Such methods have various advantages. Often, obtaining clinical : samples from certain areas in the body that is diseased can be difficult and may involve undesirable invasions in the body, for example biopsy is often used to obtain samples for cancer. In some cases, such as in Alzheimer's disease the diseased brain specimen can only be obtained post-mortem. Furthermore, the tissue specimens which are obtained are often heterogeneous and may contain a mixture of both diseased and non-diseased cells, making the analysis of generated gene expression data both complex and difficult.

It has been suggested that a pool of tumour tissues that appear to be pathogenetically homogeneous with respect to morphological appearances of the tumour may well be highly heterogeneous at the molecular level (Alizadeh, 2000, supra), and in fact might contain tumours representing essentially different diseases (Alizadeh, 2000, supra; Golub, 1999, supra). For the purpose of identifying a disease, condition, or a stage thereof, any method that does not : require clinical samples to originate directly from diseased tissues or cells is highly desirable since clinical samples representing a homogeneous mixture of cell types can be obtained from an easily accessible region in the body.

We have now identified a family of sequences which allow the derivation of a set of probes of surprising utility for identifying cancer, particularly breast cancer. Thus, we now describe families of genes whose expression is altered in "the cells of blood samples from cancer patients, which may be used to generate probes for use in methods of identifying, diagnosing or monitoring cancer or stages thereof.

In work leading up to this invention, the inventors examined the level of expression of a large number of genes in cancer patients relative to normal patients. Not only were a large number of genes found to exhibit altered expression, but, in addition, those which exhibited altered expression were found to fall within discrete families of genes, by virtue of their function. As such these genes provide a pool from which corresponding probes may be generated which can be used collectively to generate a fingerprint of the expression of these genes in an individual. Since the expression of these genes is altered in the cancer individual, and may hence be considered informative for that state, the generated fingerprint from the collection of probes is indicative of the disease relative to the normal state.

The families of genes that have been identified as being differentially expressed in cancer patients may be summarized as follows: (i) genes encoding proteins involved in protein synthesis and/or stability; (ii) genes encoding proteins involved in the regulation of defence and/or chromatin remodelling.

Family (i) includes: (a) genes encoding ribosomal proteins and ribosomal activation proteins (ie. proteins comprising components of ribosomal proteins or involved in modification of their function and are found to be down-regulated in cancer patients). These encoded proteins include ribosomal proteins L1-

L56, L7A, L10A, L13A, L18A, L23A, L27A, L35A, L36A, L37A, PO,

P1, P2, S2-S29, S31, S33-S36, S3A, S154, S18A, S18B, S18C, S27A, 63, 115 (and pseudogenes), ribosomal protein kinases (e.g. S6 kinase), ribonucleases, putative S1 RNA binding domain protein, eukaryotic translation initiation factors and guanine nucleotide binding protein G;

(b) genes encoding translation inhibition and initiation factors (ie. proteins involved in the translation of mRNA to a protein product and are found to be down-regulated in cancer patients). These encoded proteins include eukaryotic translation elongation factors, tRNA synthetases, 5 RNA binding proteins, polyadenylation element binding proteins, tyrosine phosphatases, eukaryotic translation initiation factors, and RNA polymerase I, III transcription factors; (c) genes encoding other modulators of transcription or translation such as cyclin D-type binding protein and guanine nucleotide binding protein.

Family (ii) includes: (a) genes encoding immune response related proteins (ie. proteins which are up-regulated in response to immune stimulation, and which include proteins upregulated in response to inflammation or in generating an inflammatory response, and are found to be up-regulated in cancer patients). These encoded proteins include T-cell receptor and associated components, e.g. protein kinases, various cytokines, including the interleukins and their receptors (such as IL-1, 2, 3,4, 5,6, 7,8, 9, 10, 11, 12, 13, 15, 17, 18, 20, 22, 24), tumour necrosis factor and its receptor and its superfamily (e.g. TNF superfamily members 2,3, 4,5,6,7, 8,9, 11, 12, 13, 14, 15), interferon regulatory factors, oncostatin M, Leukemia inhibitory factor, chemokine ligand and receptor family (e.g. numbers 1- 28), complement components, interferon stimulated factors such as transcription factors, MHC (e.g HLA) class I or II (or related components) (e.g. DQ, DR, DO, DP, DM alpha or beta), adhesion proteins (e.g. CD1A, CDIC, CD1D, CD3Z, 6, 8, 11, 14, 18, 24, 27, 28, 29, 40, 44, 50, 54, 59, 74, 79B, 80, 81, 83, 86, 96, ICAM), nuclear factor of kappa polypeptide gene enhancer in B-cells, myelin basic protein, cathepsin, toll-like receptor, proteosome subunits, ferritin, protein kinases or phosphatases as well as their activators and inhibitors, leukocyte immunoglobulin-like receptor, immunoglobulin components, e.g. heavy chain or Fc fragments, e.g. of IgG, IgE or IgA or their superfamily, defensin, oxytocin, S100 calcium binding protein, lectin and its receptor and superfamily, leptin, phospholipase and growth factors (such as endothelial cell growth factor or erythropoietin); (b) genes encoding TNF-induced proteins (ie. proteins which are induced in an individual in response to exposure to TNF and are found to be up- regulated in cancer patients). These encoded proteins include TNF alpha-induced protein 8, integrin, inhibitor of kappa light polypeptide gene enhancer in B-cells, TNF-associated factor 2, 5, nuclear factor of kappa light polypeptide gene enhancer in B-cells, MAP kinases, protein kinase C, ubiquitous kinase, cadherin, caspase, cyclin D1, superoxide dismutase and interleukins; (c) genes encoding hypoxia-induced proteins (ie. proteins which are induced when the individual or a part thereof is in a state of hypoxia and are found to be up-regulated in cancer patients). These encoded proteins include sestrin, B1A binding protein p300, endothelin, ataxia telangiectasia and Rad3 related protein, hexokinase 2, TEK tyrosine kinase, DNA. fragmentation factor, caspase, plasminogen activator, hypoxia-inducible factor 1 and glucose phosphate isomerase; (d) genes encoding oxidative stress proteins (ie. proteins which are induced in an individual or part thereof under oxidative stress and are found to be up-regulated in cancer patients). These encoded proteins include superoxide dismutase, glutathione synthetase, catalase, lactoperoxidase, thyroid peroxidase, myeloperoxidase, eosinophil peroxidase, oxidation resistance 1, peroxiredoxin, cytochrome P450, scavenger receptor, paraoxonase, glutathione reductase, NAD(P)H dehydrogenase, glutathione S-transferase, catenin, glutaredoxin, heat shock proteins (such as heat shock transcription factors), mitogen- activated protein kinases, enolase, thioredoxin reductase and peroxiredoxin; (e) genes encoding proteins involved in chromatin remodelling (ie. proteins which are instrumental in maintaining or modifying chromatin structure and may be essential for gene regulation). These encoded proteins include histone replacement proteins, e.g. H3.3A or H3.3B family.

Appropriate gene sequences falling within the families described above may be identified by interrogation of appropriate databases using as keywords the family name, e.g. "immune response" on gene or protein databases at

National Centre for Biotechnology Information, Norway. For confirmation of the utility of such gene sequences for the development of oligonucleotides for the tests described herein, the expression of a particular gene sequence may be assessed in a test cancer patient versus a normal patient. Variation in expression above or below control levels is indicative of the utility of the sequence for probe derivation.

Generally the genes encoding the above (i) families are down-regulated in cancer versus normal patients and in the case of (ii) families the encoding genes are up-regulated.

It is speculated that in cancer patients the systematic decreased expression of genes involved in ribosome production and translation control may indicate that blood cells are responding to a new condition in those patients by decreasing the rate of protein synthesis which may be a cellular adaption to an environment of low oxygen and energy deficiency. This is supported by the observation that genes involved in defence against reactive oxygen species (ROS) such as MnSOD and ferritin are upregulated in cancer samples. Low erythropoietin may explain the low oxygen levels in cancer patients. TNF activation is also believed to be a route for the changes in the families of genes described above since TNF is known to up regulate expression of e.g. ferritin, defensin, MnSOD and calgranulin B. TNF also inhibits BPO production which can itself cause a low oxygen condition in the blood environment. Hypoxia is "Known to induce TNF levels. These changes may be triggered by angiogenic factors entering the bloodstream. Although not wishing to be bound by theory, the hypothesis underlying the above described effects is shown in Figure 1.

Thus the invention provides a set of oligonucleotide probes which correspond to genes in a cell whose expression is affected in a pattern characteristic of a particular cancer or stage of, wherein said genes are systemically affected by said cancer or stage thereof. Preferably said genes are constitutively moderately or highly expressed. Preferably the genes are moderately or highly expressed in the cells of the sample but not in cells from disease cells or in cells having contacted such disease cells.

Such probes, particularly when isolated from cells distant to the site of disease, do not rely on the development of disease to clinically recognizable levels and allow detection of a cancer or stage thereof very early after the onset of said cancer, even years before other subjective or objective symptoms appear.

As used herein "systemically" affected genes refers to genes whose expression is affected in the body without direct contact with a disease cell or disease site and the cells under investigation are not disease cells. "Contact" as referred to herein refers to cells coming into close proximity with one another such that the direct effect of one cell on the other may be observed, e.g. an immune response, wherein these responses are not mediated by secondary molecules released from the first cell over a large distance to affect the second cell. Preferably contact refers to physical contact, or contact that is as close as is sterically possible, conveniently, cells which contact one another are found in the same unit volume, for example within 1cm’.

A "disease cell” is a cell manifesting phenotypic changes and is present at the disease site at some time during its life-span, e.g. a tumour cell at the tumour site or which has disseminated from the tumour, or a brain cell in the case of cancer of the brain. "Moderately or highly" expressed genes refers to those present in resting cells in a copy number of more than 30-100 copies/cell (assuming an average 3x10° mRNA molecules in a cell).

Specific probes having the above described properties are provided "herein.

Thus in one aspect, the present invention provides a set of oligonucleotide probes, wherein said set comprises at least 10 oligonucleotides selected from: an oligonucleotide corresponding to a gene sequence from family (i) or (ii) as defined hereinbefore or derived from such a sequence, or an

S oligonucleotide with a complementary sequence, or a functionally equivalent oligonucleotide.

The invention further provides a method of preparing a set of oligonucleotides for use in the methods described herein, comprising the step of selecting one or more oligonucleotides corresponding to a gene sequence from family (i) and one or more oligonucleotides corresponding to a gene sequence from family (ii). Preferably more than 1 oligonucleotides is selected from each family (e.g. from different sub-families) and the selected oligonucleotides are from preferred genes as described herein.

The invention also provides one or more oligonucleotide probes, wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2, 3 or 4 (e.g. from Table 2) or derived from a sequence described in Table 2, 3 or 4, or a complementary sequence thereof. Said derived oligonucleotides include oligonucleotides derived from the genes corresponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the

Accession numbers), or the complementary sequences thereof. The use of such probes in products and methods of the invention, form further aspects of the invention.

As referred to herein an "oligonucleotide" is a nucleic acid molecule having at least 6 monomers in the polymeric structure, ie. nucleotides or modified forms thereof The nucleic acid molecule may be DNA, RNA or PNA (peptide nucleic acid) or hybrids thereof or modified versions thereof, e.g. chemically modified forms, e.g. LNA (Locked Nucleic acid), by methylation or made up of modified or non-natural bases during synthesis, providing they retain their ability to bind to complementary sequences. Such oligonucleotides are used in accordance with the invention to probe target sequences and are thus "referred to herein also as oligonucleotide probes or simply as probes.

An oligonucleotide corresponding to a gene sequence from family (i) or (ii) refers to an oligonucleotide corresponding to all or a part of said gene sequence or its transcript. When a part of the gene sequence is used, it satisfies the requirements of the oligonucleotide probes as described herein, e.g. in length and function. Preferably said parts have the size described hereinafter. Said oligonucleotide is referred to hereinafter as the primary oligonucleotide. A derived oligonucleotide refers to an oligonucleotide which is a part of the primary oligonucleotide but satisfies the requirements for probes as described herein. ) Preferably the oligonucleotide probes forming said set are at least 15 bases in length to allow binding of target molecules. Especially preferably said oligonucleotide probes are from 20 to 200 bases in length, e.g. from 30 to 150 bases, preferably 50-100 bases in length. . As referred to herein the term "complementary sequences" refers to 10 sequences with consecutive complementary bases (ie. T:A, G:C) and which complementary sequences are therefore able to bind to one another through their complementarity.

Reference to "10 oligonucleotides” refers to 10 different oligonucleotides. Whilst an oligonucleotide from a gene sequence family as described herein, a derived oligonucleotide and their functional equivalent are considered different oligonucleotides, complementary oligonucleotides are not considered different. Preferably however, the at least 10 oligonucleotides correspond to 10 different gene sequences within the described gene sequence families (or derived oligonucleotides or their functional equivalents). Thus said 10 different oligonucleotides are preferably able to bind to 10 different transcripts.

Preferably the at least 10 oligonucleotides are made up of a combination of oligonucleotides from family (i) and (ii), e.g. 5 oligonucleotides from each family may be used, or 4 from one family and 6 from the other family. This advantageously allows the use of genes which are up and down-regulated in cancer relative to normal patients. Conveniently, one or more oligonucleotides "from different sub-families may be used, e.g. 2 probes each from (i)a, (Db, (ic, (ii)a and (ii)b. Especially preferably said set of oligonucleotides includes oligonucleotides from family (i)a, (ii)a and (ii)e. 30 . Preferred proteins encoded by family (i)a genes are ribosomal proteins and preferably each set includes an oligonucleotide from a gene encoding such a protein.

Preferred immune response proteins encoded by family (ii)a genes include adhesion proteins, interleukins their receptors and superfamily, TNF its receptor and superfamily, immunoglobulin components and erythropoietin.

Particularly preferably said set includes oligonucleotides from genes encoding one or more ribosomal proteins and optionally one or more histones and optionally ferritin.

Preferably said oligonucleotides are as described in Table 2 or 3 or are derived from a sequence described in Table 2 or 3, e.g. as described in Table 2.

Said set may additionally comprise one or more oligonucleotide probes listed in

Table 4, or derived from a sequence described in Table 4, or a complementary sequence thereof. Said derived oligonucleotides include oligonucleotides derived from the genes corresponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the Accession numbers), or the complementary sequences thereof.

A "set" as described refers to a collection of unique oligonucleotide probes (ie. having a distinct sequence) and preferably consists of less than 1000 oligonucleotide probes, especially less than 500 probes, e.g. preferably from 10 to 500, e.g. 10 to 100, 200 or 300, especially preferably 20 to 100, e.g. 30 to 100 probes. In some cases less than 10 probes may be used, e.g. from 2 to 9 probes, e.g. 5 to 9 probes.

Tt will be appreciated that increasing the number of probes will prevent the possibility of poor analysis, €.8. misdiagnosis by comparison to other diseases which could similarly alter the expression of the particular genes in question. Other oligonucleotide probes not described herein may also be present, particularly if they aid the ultimate use of the set of oligonucleotide probes. However, preferably said set consists only of the oligonucleotides described herein, or a sub-set thereof (e.g. of the size as described above).

Multiple copies of each unique oligonucleotide probe, e.g. 10 or more copies, may be present in each set, but constitute only a single probe.

A set of oligonucleotide probes, which may preferably be immobilized on a solid support or have means for such immobilization, comprises the at least 10 oligonucleotide probes selected from those described hereinbefore. As mentioned above, these 10 probes must be unique and have different sequences.

Having said this however, two separate probes may be used which recognize the same gene but reflect different splicing events. However oligonucleotide probes which are complementary to, and bind to distinct genes are preferred.

As described herein a "functionally equivalent" or derived oligonucleotide refers to an oligonucleotide which is capable of identifying the same gene as an oligonucleotide from a sequence in the gene sequence families described herein ie. it can bind to the same mRNA molecule (or DNA) transcribed from a gene (target nucleic acid molecule) as the primary oligonucleotide or the derived oligonucleotide (or its complementary sequence).

Thus in a preferred feature said derived or functionally equivalent oligonucleotide is a part of a gene sequence as defined in Table 2, 5 or 6, or the complementary sequence thereof. Preferably said functionally equivalent oligonucleotide is capable of recognizing, ie. binding to the same splicing product as a primary oligonucleotide or a derived oligonucleotide. Preferably said mRNA molecule is the full length mRNA molecule which corresponds to the primary oligonucleotide or the derived oligonucleotide.

As referred to herein "capable of binding" or "binding" refers to the ability to hybridize under conditions described hereinafter.

Alternatively expressed, functionally equivalent oligonucleotides (or complementary sequences) have sequence identity or will hybridize, as described hereinafter, to a region of the target molecule to which molecule a primary oligonucleotide or a derived oligonucleotide or a complementary oligonucleotide binds. Preferably, functionally equivalent oligonucleotides (or their complementary sequences) hybridize to one of the mRNA sequences which corresponds to a primary oligonucleotide or a derived oligonucleotide under the conditions described hereinafter or has sequence identity to a part of one of the mRNA sequences which corresponds to a primary oligonucleotide or a derived oligonucleotide. A "part" in this context refers to a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10 to 50 or 15 to 30 bases.

In a particularly preferred aspect, the functionally equivalent oligonucleotide binds to all or a part of the region of a target nucleic acid molecule (mRNA or cDNA) to which the primary oligonucleotide or derived oligonucleotide binds. A "target" nucleic acid molecule is the gene transcript or related product e.g. mRNA, or cDNA, or amplified product thereof. Said "region" of said target molecule to which said primary oligonucleotide or derived oligonucleotide binds is the stretch over which complementarity exists.

At its largest this region is the whole length of the primary oligonucleotide or derived oligonucleotide, but may be shorter if the entire primary sequence or derived oligonucleotide is not complementary to a region of the target sequence.

Preferably said part of said region of said target molecule is a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10to 50 or 15 to 30 bases. This may for example be achieved by said functionally equivalent oligonucleotide having several identical bases to the bases of the primary oligonucleotide or the derived oligonucleotide. These bases may be identical over consecutive stretches, e.g. in a part of the functionally equivalent oligonucleotide, or may be present non-consecutively, but provide sufficient complementarity to allow binding to the target sequence.

Thus in a preferred feature, said functionally equivalent oligonucleotide hybridizes under conditions of high stringency to a primary oligonucleotide or a derived oligonucleotide or the complementary sequence thereof. Alternatively expressed, said functionally equivalent oligonucleotide exhibits high sequence identity to all or part of a primary oligonucleotide. Preferably said functionally equivalent oligonucleotide has at least 70% sequence identity, preferably at least 80%, e.g. at least 90, 95, 98 or 99%, to all of a primary oligonucleotide or a part thereof. As used in this context, a "part" refers to a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10 to 50 or 15to 30 bases, in said primary oligonucleotide. Bspecially preferably when sequence identity to only a part of said primary oligonucleotide is present, the sequence identity is high, e.g. at least 80% as described above.

Functionally equivalent oligonucleotides which satisfy the above stated functional requirements include those which are derived from the primary oligonucleotides and also those which have been modified by single or multiple nucleotide base (or equivalent) substitution, addition and/or deletion, but which nonetheless retain functional activity, e.g. bind to the same target molecule as the primary oligonucleotide or the derived oligonucleotide from which they are further derived or modified. Preferably said modification is of from 1 to 50, e.g. from 10 to 30, preferably from 1 to 5 bases. Especially preferably only minor modifications are present, e.g. variations in less than 10 bases, e.g. less than 5 base changes.

Within the meaning of "addition" equivalents are included oligonucleotides containing additional sequences which are complementary to the consecutive stretch of bases on the target molecule to which the primary oligonucleotide or the derived oligonucleotide binds. Alternatively the addition may comprise a different, unrelated sequence, which may for example confer a further property, e.g. to provide a means for immobilization such as a linker to bind the oligonucleotide probe to a solid support.

Particularly preferred are naturally occurring equivalents such as biological variants, e.g. allelic, geographical or allotypic variants, e.g. oligonucleotides which correspond to a genetic variant, for example as present in a different species.

Functional equivalents include oligonucleotides with modified bases, e.g. using non-naturally occurring bases. Such derivatives may be prepared during synthesis or by post production modification. “Hybridizing" sequences which bind under conditions of low stringency are those which bind under non-stringent conditions (for example, 6x SSC/50% formamide at room temperature) and remain bound when washed under conditions of low stringency (2 X SSC, room temperature, more preferably 2 X

SSC, 42°C). Hybridizing under high stringency refers to the above conditions in which washing is performed at 2 X SSC, 65°C (where SSC = 0.15M NaCl, " 0.015M sodium citrate, pH 7.2). “Sequence identity" as referred to herein refers to the value obtained when assessed using ClustalW (Thompson et al., 1994, Nucl. Acids Res., 22, p4673-4680) with the following parameters:

Pairwise alignment parameters - Method: accurate, Matrix: TUB, Gap open penalty: 15.00, Gap extension penalty: 6.66;

Multiple alignment parameters - Matrix: TUB, Gap open penalty: 15.00, % identity for delay: 30, Negative matrix: no, Gap extension penalty: 6.66, DNA transitions weighting: 0.5.

Sequence identity at a particular base is intended to include identical bases which have simply been derivatized.

The invention also extends to polypeptides encoded by the mRNA sequence to which a Table 2, 3 or 4 oligonucleotide or a Table 2, 3 or 4 derived oligonucleotide (e.g. having a sequence as defined in Table 2, Sor6 ora complementary sequence thereto) binds. The invention further extends to antibodies which bind to any of said polypeptides.

As described above, conveniently said set of oligonucleotide probes may be immobilized on one or more solid supports. Single or preferably multiple copies of each unique probe are attached to said solid supports, e.g. 10 or more, e.g. at least 100 copies of each unique probe are present.

One or more unique oligonucleotide probes may be associated with separate solid supports which together form a set of probes immobilized on multiple solid support, e.g. one or more unique probes may be immobilized on multiple beads, membranes, filters, biochips etc. which together form a set of probes, which together form modules of the kit described hereinafter. The solid support of the different modules are conveniently physically associated although the signals associated with each probe (generated as described hereinafter) must be separately determinable. Alternatively, the probes may be immobilized on discrete portions of the same solid support, e.g. each unique oligonucleotide probe, e.g. in multiple copies, may be immobilized fo a distinct and discrete portion or region of a single filter or membrane, e.g. to generate an array.

A combination of such techniques may also be used, e.g. several solid supports may be used which each immobilize several unique probes.

The expression "solid support" shall mean any solid material able to bind oligonucleotides by hydrophobic, ionic or covalent bridges. "Immobilization" as used herein refers to reversible or irreversible association of the probes to said solid support by virtue of such binding. If reversible, the probes remain associated with the solid support for a time sufficient for methods of the invention to be carried out.

Numerous solid supports suitable as immobilizing moieties according to the invention, are well known in the art and widely described in the literature and generally speaking, the solid support may be any of the well-known supports or matrices which are currently widely used or proposed for immobilization, separation etc. in chemical or biochemical procedures. Such materials include, but are not limited to, any synthetic organic polymer such as polystyrene, polyvinylchloride, polyethylene; or nitrocellulose and cellulose acetate; or tosyl activated surfaces; or glass or nylon or any surface carrying a group suited for covalent coupling of nucleic acids. The immobilizing moieties may take the form of particles, sheets, gels, filters, membranes, microfibre strips, tubes or plates, fibres or capillaries, made for example of a polymeric material e.g. agarose, cellulose, alginate, teflon, latex or polystyrene or magnetic beads. Solid supports allowing the presentation of an array, preferably in a single dimension are preferred, e.g. sheets, filters, membranes, plates or biochips.

Attachment of the nucleic acid molecules to the solid support may be performed directly or indirectly. For example if a filter is used, attachment may be performed by UV-induced crosslinking. Alternatively, attachment may be performed indirectly by the use of an attachment moiety carried on the oligonucleotide probes and/or solid support. Thus for example, a pair of affinity binding partners may be used, such as avidin, streptavidin or biotin, DNA or

DNA binding protein (e.g. either the lac I repressor protein or the lac operator sequence to which it binds), antibodies (which may be mono- or polyclonal), antibody fragments or the epitopes or haptens of antibodies. In these cases, one partner of the binding pair is attached to (or is inherently part of) the solid support and the other partner is attached to (or is inherently part of) the nucleic acid molecules.

As used herein an "affinity binding pair" refers to two components which recognize and bind to one another specifically (ic. in preference to binding to other molecules). Such binding pairs when bound together form a complex.

Attachment of appropriate functional groups to the solid support may be performed by methods well known in the art, which include for example, attachment through hydroxyl, carboxyl, aldehyde or amino groups which may be provided by treating the solid support to provide suitable surface coatings. Solid supports presenting appropriate moieties for attachment of the binding partner may be produced by routine methods known in the art.

Attachment of appropriate functional groups to the oligonucleotide probes of the invention may be performed by ligation or introduced during synthesis or amplification, for example using primers carrying an appropriate moiety, such as biotin or a particular sequence for capture.

Conveniently, the set of probes described hereinbefore is provided in kit form.

Thus viewed from a further aspect the present invention provides a kit comprising a set of oligonucleotide probes as described hereinbefore immobilized on one or more solid supports.

Preferably, said probes are immobilized on a single solid support and each unique probe is attached to a different region of said solid support.

However, when attached to multiple solid supports, said multiple solid supports form the modules which make up the kit. Especially preferably said solid support is a sheet, filter, membrane, plate or biochip.

Optionally the kit may also contain information relating to the signals generated by normal or diseased samples (as discussed in more detail hereinafter in relation to the use of the kits), standardizing materials, e.g. mRNA or cDNA from normal and/or diseased samples for comparative purposes, labels for incorporation into cDNA, adapters for introducing nucleic acid sequences for amplification purposes, primers for amplification and/or appropriate enzymes, "buffers and solutions. Optionally said kit may also contain a package insert describing how the method of the invention should be performed, optionally providing standard graphs, data or software for interpretation of results obtained when performing the invention.

The use of such kits to prepare a standard diagnostic gene transcript pattern as described hereinafter forms a further aspect of the invention.

The set of probes as described herein have various uses. Principally however they are used to assess the gene expression state of a test cell to provide information relating to the organism from which said cell is derived. Thus the probes are useful in diagnosing, identifying or monitoring a cancer or stage thereof in an organism.

Thus in a further aspect the invention provides the use of a set of oligonucleotide probes or a kit as described hereinbefore to determine the gene expression pattern of a cell which pattern reflects the level of gene expression of genes to which said oligonucleotide probes bind, comprising at least the steps of: a) isolating mRNA from said cell, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of ol igonucleotide probes or a kit as defined herein, and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern.

The mRNA and cDNA as referred to in this method, and the methods hereinafter, encompass derivatives or copies of said molecules, e.g. copies of such molecules such as those produced by amplification or the preparation of complementary strands, but which retain the identity of the mRNA sequence, ie. would hybridize to the direct transcript (or its complementary sequence) by virtue of precise complementarity, or sequence identity, over at least a region of said molecule. It will be appreciated that complementarity will not exist over the entire region where techniques have been used which may truncate the transcript or introduce new sequences, e.g. by primer amplification. For convenience, said mRNA or cDNA is preferably amplified prior to step b). As with the oligonucleotides described herein said molecules may be modified, e.g. by using non-natural bases during synthesis providing complementarity remains. Such molecules may also carry additional moieties such as signalling or immobilizing means.

The various steps involved in the method of preparing such a pattern are described in more detail hereinafter.

As used herein "gene expression” refers to transcription of a particular gene to produce a specific mRNA product (ie. a particular splicing product).

The level of gene expression may be determined by assessing the level of transcribed mRNA molecules or cDNA molecules reverse transcribed from the mRNA molecules or products derived from those molecules, e.g. by amplification.

The "pattern" created by this technique refers to information which, for example, may be represented in tabular or graphical form and conveys information about the signal associated with two or more oligonucleotides.

Preferably said pattern is expressed as an array of numbers relating to the expression level associated with each probe.

Preferably, said pattern is established using the following linear model: y=Xb+f Equation 1 wherein, X is the matrix of gene expression data and y is the response variable, b is the regression coefficient vector and f the estimated residual vector. Although many different methods can be used to establish the relationship provided in equation 1, especially preferably the partial Least Squares Regression (PLSR) method is used for establishing the relationship in equation 1.

The probes are thus used to generate a pattern which reflects the gene expression of a cell at the time of its isolation. The pattern of expression 1s characteristic of the circumstances under which that cells finds itself and depends on the influences to which the cell has been exposed. Thus, a characteristic gene transcript pattern standard or fingerprint (standard probe pattern) for cells from an individual with a particular cancer may be prepared and used for comparison to transcript patterns of test cells. This has clear applications in diagnosing, monitoring or identifying whether an organism is suffering from a particular cancer or stage thereof.

The standard pattern is prepared by determining the extent of binding of total mRNA (or cDNA or related product), from cells from a sample of one or more organisms with the cancer or stage thereof, to the probes. This reflects the level of transcripts which are present which correspond to each unique probe.

The amount of nucleic acid material which binds to the different probes is assessed and this information together forms the gene transcript pattern standard of that cancer or stage thereof. Each such standard pattern is characteristic of the cancer or stage thereof.

In a further aspect therefore, the present invention provides a method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) isolating mRNA from the cells of a sample of one or more organisms having the cancer or stage thereof, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides or a kit as described hereinbefore specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattem reflecting the level of gene expression of genes to which said oligonucleotides bind, in the sample with the cancer or stage thereof.

For convenience, said oligonucleotides are preferably immobilized on one or more solid supports.

The standard pattern for a great number of cancers and different stages thereof using particular probes may be accumulated in databases and be made available to laboratories on request. "Disease" samples and organisms or "cancer" samples and organisms as referred to herein refer to organisms (or samples from the same) with abnormal cell proliferation e.g, in a solid mass such as a tumour. Such organisms are known to have, or which exhibit, the cancer or stage thereof under study. "Stages" thereof refer to different stages of the cancer which may or may not exhibit particular physiological or metabolic changes, but do exhibit changes at the genetic level which may be detected as altered gene expression. It will be appreciated that during the course of a cancer the expression of different transcripts may vary. Thus at different stages, altered expression may not be exhibited for particular transcripts compared to "normal" samples. However,

combining information from several transcripts which exhibit altered expression at one or more stages through the course of the cancer can be used to provide a characteristic pattern which is indicative of a particular stage of the cancer. Thus for example different stages in cancer, €.g. pre-stage 1, stage I, stage II, II or v can be identified. "Normal" as used herein refers to organisms or samples which are used for comparative purposes. Preferably, these are "normal” in the sense that they do not exhibit any indication of, or are not believed to have, any disease or condition that would affect gene expression, particularly in respect of cancer for which they are to be used as the normal standard. However, it will be appreciated that different stages of a cancer may be compared and in such cases, the "normal" sample may correspond to the earlier stage of the cancer.

As used herein a "sample" refers to any material obtained from the organism, e.g. human or non-human animal under investigation which contains cells and includes, tissues, body fluid or body waste or in the case of prokaryotic organisms, the organism itself. "Body fluids" include blood, saliva, spinal fluid, semen, lymph. "Body waste" includes urine, expectorated matter (pulmonary patients), faeces etc. "Tissue samples" include tissue obtained by biopsy, by surgical interventions or by other means e.g. placenta. Preferably however, the samples which are examined are from areas of the body not apparently affected by the cancer. The cells in such samples are not disease cells, i.e. cancer cells, have not been in contact with such disease cells and do not originate from the site of the cancer. The "site of disease" is considered to be that area of the body which manifests the disease in a way which may be objectively determined, e.g. a tumour. Thus for example peripheral blood may be used for the diagnosis of non-haematopoietic cancers, and the blood does not require the presence of malignant or disseminated cells from the cancer in the blood. Similarly in diseases of the brain, in which no diseased cells are found in the blood due to the blood-brain barrier, peripheral blood may still be used in the methods of the invention.

It will however be appreciated that the method of preparing the standard transcription pattern and other methods of the invention are also applicable for use on living parts of eukaryotic organisms such as cell lines and organ cultures and explants.

As used herein, reference to "corresponding" sample etc. refers to cells preferably from the same tissue, body fluid or body waste, but also includes cells 5 . from tissue, body fluid or body waste which are sufficiently similar for the purposes of preparing the standard or test pattem. When used in reference to genes "corresponding" to the probes, this refers to genes which are related by sequence (which may be complementary) to the probes although the probes may reflect different splicing products of expression. "Assessing" as used herein refers to both quantitative and qualitative assessment which may be determined in absolute or relative terms.

The invention may be put into practice as follows.

To prepare a standard transcript pattern for a particular cancer or stage thereof, sample mRNA is extracted from the cells of tissues, body fluid or body waste according to known techniques (see for example Sambrook et. al. (1989),

Molecular Cloning : A laboratory manual, 2nd Ed., Cold Spring Harbor

Laboratory Press, Cold Spring Harbor, N.Y.) from a diseased individual or organism.

Owing to the difficulties in working with RNA, the RNA is preferably reverse transcribed at this stage to form first strand cDNA. Cloning of the cDNA or selection from, or using, a cDNA library is not however necessary in this or other methods of the invention. Preferably, the complementary strands of the first strand cDNAs are synthesized, ie. second strand cDNAs, but this will depend on which relative strands are present in the oligonucleotide probes. The

RNA may however alternatively be used directly without reverse transcription and may be labelled if so required.

Preferably the cDNA strands are amplified by known amplification techniques such as the polymerase chain reaction (PCR) by the use of appropriate primers. Alternatively, the cDNA strands may be cloned with a vector, used to transform a bacteria such as E. coli which may then be grown to multiply the nucleic acid molecules. ‘When the sequence of the cDNAs are not known, primers may be directed to regions of the nucleic acid molecules which have been introduced. Thus for example, adapters may be ligated to the cDNA molecules and primers directed to these portions for amplification of the cDNA molecules. Alternatively, in the case of eukaryotic samples, advantage may be taken of the polyA tail and cap of the RNA to prepare appropriate primers.

S To produce the standard diagnostic gene transcript pattern or fingerprint for a particular cancer or stage thereof, the above described oligonucleotide probes are used to probe mRNA or cDNA of the diseased sample to produce a signal for hybridization to each particular oligonucleotide probe species, ie. each unique probe. A standard control gene transcript pattern may also be prepared if desired using mRNA or cDNA from a normal sample. Thus, mRNA or cDNA is brought into contact with the oligonucleotide probe under appropriate conditions to allow hybridization.

When multiple samples are probed, this may be performed consecutively using the same probes, e.g. on one or more solid supports, ie. on probe kit modules, or by simultaneously hybridizing to corresponding probes, e.g. the modules of a corresponding probe kit.

To identify when hybridization occurs and obtain an indication of the number of transcripts/cDNA molecules which become bound to the oligonucleotide probes, it is necessary to identify a signal produced when the transcripts (or related molecules) hybridize (e.g. by detection of double stranded nucleic acid molecules or detection of the number of molecules which become bound, after removing unbound molecules, e.g. by washing).

In order to achieve a signal, either or both components which hybridize (ie. the probe and the transcript) carry or form a signalling means or a part thereof. This "signalling means" is any moiety capable of direct or indirect detection by the generation or presence of a signal. The signal may be any "detectable physical characteristic such as conferred by radiation emission, scattering or absorption properties, magnetic properties, or other physical properties such as charge, size or binding properties of existing molecules (e.g. labels) or molecules which may be generated (e.g. gas emission etc.).

Techniques are preferred which allow signal amplification, e.g. which produce multiple signal events from a single active binding site, e.g. by the catalytic action of enzymes to produce multiple detectable products.

Conveniently the signalling means may be a label which itself provides a detectable signal. Conveniently this may be achieved by the use of a radioactive or other label which may be incorporated during cDNA production, the preparation of complementary cDNA strands, during amplification of the target mRNA/cDNA or added directly to target nucleic acid molecules.

Appropriate labels are those which directly or indirectly allow detection or measurement of the presence of the transcripts/cDNA. Such labels include for example radiolabels, chemical labels, for example chromophores or fluorophores (e.g. dyes such as fluorescein and rhodamine), or reagents of high electron density such as ferritin, haemocyanin or colloidal gold. Alternatively, the label may be an enzyme, for example peroxidase or alkaline phosphatase, wherein the presence of the enzyme is visualized by its interaction with a suitable entity, for example a substrate. The label may also form part of a signalling pair wherein the other member of the pair is found on, or in close proximity to, the oligonucleotide probe to which the transcript/cDNA binds, for example, a fluorescent compound and a quench fluorescent substrate may be used. A label may also be provided on a different entity, such as an antibody, which recognizes a peptide moiety attached to the transcripts/cDNA, for example attached to a base used during synthesis or amplification.

A signal may be achieved by the introduction of a label before, during or after the hybridization step. Alternatively, the presence of hybridizing transcripts may be identified by other physical properties, such as their absorbance, and in which case the signalling means is the complex itself.

The amount of signal associated with each oligonucleotide probe is then "assessed. The assessment may be quantitative or qualitative and may be based on binding of a single transcript species (or related cDNA or other products) to each probe, or binding of multiple transcript species to multiple copies of each unique probe. It will be appreciated that quantitative results will provide further information for the transcript fingerprint of the cancer which is compiled. This data may be expressed as absolute values (in the case of macroarrays) or may be determined relative to a particular standard or reference e.g. a normal control sample.

Furthermore it will be appreciated that the standard diagnostic gene pattern transcript may be prepared using one or more disease samples (and normal samples if used) to perform the hybridization step to obtain patterns not biased towards a particular individual's variations in gene expression.

The use of the probes to prepare standard patterns and the standard diagnostic gene transcript patterns thus produced for the purpose of identification or diagnosis or monitoring of a particular cancer or stage thereof in a particular organism forms a further aspect of the invention.

Once a standard diagnostic fingerprint or pattern has been determined for a particular cancer or stage thereof using the selected oligonucleotide probes, this information can be used to identify the presence, absence or extent or stage of that cancer in a different test organism or individual.

To examine the gene expression pattern of a test sample, a test sample of tissue, body fluid or body waste containing cells, corresponding to the sample used for the preparation of the standard pattern, is obtained from a patient or the organism to be studied. A test gene transcript pattern is then prepared as described hereinbefore as for the standard pattern.

In a further aspect therefore, the present invention provides a method of preparing a test gene transcript pattern comprising at least the steps of: a) isolating mRNA from the cells of a sample of said test organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (2) to a set of oligonucleotides or a kit as described hereinbefore specific for a cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation, and ¢) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said test sample.

This test pattern may then be compared to one or more standard pattems to assess whether the sample contains cells having the cancer or stage thereof.

Thus viewed from a further aspect the present invention provides a method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism, comprising the steps of: a) isolating mRNA from the cells of a sample of said organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides or a kit as described hereinbefore specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation; c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said sample; and d) comparing said pattern to a standard diagnostic pattern prepared according to the method of the invention using a sample from an organism corresponding to the organism and sample under investigation to determine the presence of said cancer or a stage thereof in the organism under investigation.

The method up to and including step c) is the preparation of a test pattern as described above.

As referred to herein, "diagnosis" refers to determination of the presence or existence of a cancer or stage thereof in an organism. "Monitoring" refers to establishing the extent of a cancer, particularly when an individual is known to be suffering from cancer, for example to monitor the effects of treatment or the development of a cancer, e.g. to determine the suitability of a treatment or provide a prognosis.

The presence of the cancer or stage thereof may be determined by determining the degree of correlation between the standard and test samples’ patterns. This necessarily takes into account the range of values which are obtained for normal and diseased samples. Although this can be established by obtaining standard deviations for several representative samples binding to the probes to develop the standard, it will be appreciated that single samples may be sufficient to generate the standard pattern to identify a cancer if the test sample exhibits close enough correlation to that standard. Conveniently, the presence,

absence, or extent of a cancer or stage thereof in a test sample can be predicted by inserting the data relating to the expression level of informative probes in test : sample into the standard diagnostic probe pattern established according to equation 1.

Data generated using the above mentioned methods may be analysed using various techniques from the most basic visual representation (e.g. relating to intensity) to more complex data manipulation to identify underlying pattems which reflect the interrelationship of the level of expression of each gene to which the various probes bind, which may be quantified and expressed mathematically. Conveniently, the raw data thus generated may be manipulated by the data processing and statistical methods described hereinafter, particularly normalizing and standardizing the data and fitting the data to a classification model to determine whether said test data reflects the pattern of a particular cancer or stage thereof.

The methods described herein may be used to identify, monitor or diagnose a cancer or its stage or progression, for which the oligonucleotide probes are informative. "Informative" probes as described herein, are those which reflect genes which have altered expression in the cancer in question, or particular stages thereof. Probes of the invention may not be sufficiently informative for diagnostic purposes when used alone, but are informative when used as one of several probes to provide a characteristic pattern, e.g. in a set as described hereinbefore.

Preferably said probes correspond to genes which are systemically affected by said cancer or stage thereof. Especially preferably said genes, from which transcripts are derived which bind to probes of the invention, are moderately or highly expressed. The advantage of using probes directed to moderately or highly expressed genes is that smaller clinical samples are required for generating the necessary gene expression data set, e.g. less than 1ml blood samples.

Furthermore, it has been found that such genes which are already being actively transcribed tend to be more prone to being influenced, in a positive or negative way, by new stimuli. In addition, since transcripts are already being produced at levels which are generally detectable, small changes in those levels are readily detectable as for example, a certain detectable threshold does not need to be reached.

In preferred methods of the invention, the set of probes of the invention are informative for a variety of different cancers or stages thereof. A sub-set of the probes disclosed herein may be used for diagnosis, identification or monitoring a particular cancer or stage thereof. : Cancers for which the probes may be used for diagnosis, identification and monitoring include stomach, lung, breast, prostate gland, bowel, skin, colon and ovary cancer. Especially preferably the probes are used for breast cancer analysis.

The diagnostic method may be used alone as an alternative to other diagnostic techniques or in addition to such techniques. For example, methods of the invention may be used as an alternative or additive diagnostic measure to diagnosis using imaging techniques such as Magnetic Resonance Imagine (MRI), ultrasound imaging, puclear imaging or X-ray imaging, for example in the identification and/or diagnosis of tumours.

The methods of the invention may be performed on cells from prokaryotic or eukaryotic organisms which may be any eukaryotic organisms such as human beings, other mammals and animals, birds, insects, fish and plants, and any prokaryotic organism such as a bacteria.

Preferred non-human animals on which the methods of the invention may be conducted include, but are not limited to mammals, particularly primates, domestic animals, livestock and laboratory animals. Thus preferred animals for diagnosis include mice, rats, guinea pigs, cats, dogs, pigs, cOWs, goats, sheep, horses. Particularly preferably cancer of humans is diagnosed, identified or monitored.

As described above, the sample under study may be any convenient sample which may be obtained from an organism. Preferably however, as mentioned above, the sample is obtained from a site distant to the site of disease and the cells in such samples are not disease cells, have not been in contact with such cells and do not originate from the site of the disease. In such cases,

although preferably absent, the sample may contain cells which do not fulfil these criteria. However, since the probes of the invention are concerned with transcripts whose expression is altered in cells which do satisfy these criteria, the probes are specifically directed to detecting changes in transcript levels in those cells even if in the presence of other, background cells.

It has been found that the cells from such samples show significant and informative variations in the gene expression of a large number of genes. Thus, the same probe (or several probes) may be found to be informative in determinations regarding two or more cancers, or stages thereof by virtue of the particular level of transcripts binding to that probe or the interrelationship of the extent of binding to that probe relative to other probes. As a consequence, it is possible to use a relatively small number of probes for screening for multiple cancers. This has consequences with regard to the selection of probes, but also for the use of a single set of probes for more than one diagnosis.

Thus, the present invention also provides sets of probes for diagnosing, identifying or monitoring two or more cancers or stages thereof, wherein at least one of said probes is suitable for said diagnosing, identifying or monitoring at least two of said cancers or stages thereof, and kits and methods of using the : same. Preferably at least 5 probes, e.g. from 5 to 15 probes, are used in at least two diagnoses.

Thus, in a further preferred aspect, the present invention provides a method of diagnosis or identification or monitoring as described hereinbefore for the diagnosis, identification or monitoring of two or more cancers or stages thereof in an organism, wherein said test pattem produced in step c) of the diagnostic method is compared in step d) to at least two standard diagnostic patterns prepared as described previously, wherein each standard diagnostic pattern is a pattem generated for a different cancer or stage thereof.

Whilst in a preferred aspect the methods of assessment concern the development of a gene transcript pattern from a test sample and comparison of the same to a standard pattern, the elevation or depression of expression of certain markers may also be examined by examining the products of expression :

and the level of those products. Thus a standard pattern in relation to the expressed product may be generated.

In such methods the levels of expression of a set of polypeptides encoded by the gene to which a primary oligonucleotide or a derived oligonucleotide, binds, are analysed.

Various diagnostic methods may be used to assess the amount of . polypeptides (or fragments thereof) which are present. The presence or concentration of polypeptides may be examined, for example by the use of a binding partner to said polypeptide (e.g. an antibody), which may be immobilized, to separate said polypeptide from the sample and the amount of polypeptide may then be determined. "Fragments" of the polypeptides refers to a domain or region of said polypeptide, e.g. an antigenic fragment, which is recognizable as being derived from said polypeptide to allow binding of a specific binding partner. Preferably such a fragment comprises a significant portion of said polypeptide and corresponds to a product of normal post-synthesis processing.

Thus in a further aspect thc present invention provides a method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) releasing target polypeptides froma sample of one or more organisms having the cancer or stage thereof; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in the sample with the cancer or stage thereof.

WQ 2005/118851 PCT/GB2005/002180 .

As used herein "target polypeptides” refer to those polypeptides present in a sample which are to be detected and "marker polypeptides" are polypeptides which are encoded by the genes to which primary oligonucleotides or derived oligonucleotides bind, ie. genes within the gene families. The target and marker polypeptides are identical or at least have areas of high similarity, e.g. epitopic regions to allow recognition and binding of the binding partner. "Release" of the target polypeptides refers to appropriate treatment of a sample to provide the polypeptides in a form accessible for binding of the binding partners, e.g. by lysis of cells where these are present. The samples used in this case need not necessarily comprise cells as the target polypeptides may be released from cells into the surrounding tissue or fluid, and this tissue or fluid may be analysed, e.g. urine or blood. Preferably however the preferred samples as described herein are used. "Binding partners" comprise the separate entities which together make an affinity binding pair as described above, wherein one partner of the binding pair is the target or marker polypeptide and the other partner binds specifically to that polypeptide, e.g. an antibody.

Various arrangements may be cnvisaged for detecting the amount of binding pairs which form. In its simplest form, a sandwich type assay ¢.g. an immunoassay such as an ELISA, may be used in which an antibody specific to the polypeptide and carrying a label (as described elsewhere herein) may be bound to the binding pair (e.g. the first antibody:polypeptide pair) and the amount of label detected.

Other methods as described herein may be similarly modified for analysis of the protein product of expression rather than the gene transcript and related nucleic acid molecules.

Thus a further aspect of the invention provides amethod of preparing a test gene transcript pattern comprising at least the steps of: a) releasing target polypeptides from a sample of said test organism, b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in said test sample. .

A yet further aspect of the invention provides a method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism comprising the steps of: a) releasing target polypeptides from a sampie of said organism, b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides in said sample; and d) comparing said pattern to 2 standard diagnostic pattern prepared as described hereinbefore using a sample from an organism corresponding to the organism and sample under investigation to determine the degree of correlation indicative of the presence of said cancer or a stage thereof in the organism under investigation.

The methods of generating standard and test patterns and diagnostic : techniques rely on the use of informative oligonucleotide probes to generate the gene expression data. In some cases it will be necessary to select these informative probes for a particular method, e.g. to diagnose a particular cancer, from a selection of available probes, e.g. the Table 2 and/or 3 oligonucleotides, the Table 2 and/or 3 derived oligonucleotides, their complementary sequences and functionally equivalent oligonucleotides and optionally the Table 4 oligonucleotides, their derived oligonucleotides, complementary sequences and functionally equivalent oligonucleotides. Said derived oligonucleotides include oligonucleotides derived from the genes corresponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the

Accession numbers), or the complementary sequences thereof. The following methodology describes a convenient method for identifying such informative probes, or more particularly how to select a suitable sub-set of probes from the probes described herein.

Probes for the analysis of a particular cancer or stage thereof, may be identified in a number of ways known in the prior art, including by differential expression or by library subtraction (see for example W098/49342). As described in PCT/GB03/005102 and as described hereinafter, in view of the high information content of most transcripts, as a starting point one may also simply analyse a random sub-set of mRNA or cDNA species corresponding to the family of sequence described herein and pick the most informative probes from that sub-set. The following method describes the use of immobilized oligonucleotide probes (e.g. the probes of the invention) to which mRNA (or related molecules) from different samples are bound to identify which probes are the most informative to identify a particular type of cancer, e.g. 2 disease sample.

The immobilized probes can be derived from various unrelated or related organisms; the only requirement is that the immobilized probes should bind specifically to their homologous counterparts in test organisms. Probes can also be derived from commercially available or public databases and immobilized on solid supports. The selected probes necessarily correspond to one of the genes in the gene sequence families described herein, but the probes of interest may be randomly selected from within that entire group of families.

The length of the probes immobilised on the solid support should be long enough to allow for specific binding to the target sequences. The immobilised probes can be in the form of DNA, RNA or their modified products or PNAs (peptide nucleic acids). Preferably, the probes immobilised should bind specifically to their homologous counterparts representing highly and moderately expressed genes in test organisms. Conveniently the probes which are used are the probes described herein.

The gene expression pattern of cells in biological samples can be generated using prior art techniques such as microarray or macroarray as

S described below or using methods described herein. Several technologies have now been developed for monitoring the expression level of a large number of genes simultaneously in biological samples, such as, high-density oligoarrays (Lockhart et al., 1996, Nat. Biotech., 14, p1675 -1680), cDNA microarrays (Schena et al, 1995, Science, 270, p467 -470) and cDNA macroarrays (Maier E et al., 1994, Nucl. Acids Res., 22, p3423-3424; Bernard et al., 1996, Nucl. Acids

Res., 24, p1435-1442).

In high-density oligoarrays and cDNA microarrays, hundreds and thousands of probe oligonucleotides or cDNAs, are spotted onto glass slides or nylon membranes, or synthesized on biochips. The mRNA isolated from the test and reference samples are labelled by reverse transcription with a red or green fluorescent dye, mixed, and hybridised to the microarray. After washing, the bound fluorescent dyes are detected by a laser, producing two images, one for each dye. The resulting ratio of the red and green spots on the two images provides the information about the changes in expression levels of genes in the test and reference samples. Alternatively, single channel or multiple channel microarray studies can also be performed.

In cDNA macroarray, different cDNAs are spotted on a solid support such as nylon membranes in excess in relation to the amount of test mRNA that can hybridise to each spot. mRNA isolated from test samples is radio- labelled by reverse transcription and hybridised to the immobilised probe cDNA. After washing, the signals associated with labels bybridising specifically to immobilised probe cDNA are detected and quantified. The data obtained in macroarray contains information about the relative levels of transcripts present in the test samples. Whilst macroarrays are only suitable to monitor the expression of a limited number of genes, microarrays can be used to monitor the expression of several thousand genes simultaneously and is, therefore, a preferred choice for large-scale gene expression studies.

A macroarray technique for generating the gene expression data set has been used to illustrate the probe identification method described herein. For this . purpose, mRNA is isolated from samples of interest and used to prepare labelled target molecules, e.g. mRNA or cDNA as described above. The labelled target molecules are then hybridised to probes immobilised on the solid support.

Various solid supports can be used for the purpose, as described previously.

Following hybridization, unbound target molecules are removed and signals from target molecules hybridizing to immobilised probes quantified. If radio labelling is performed, Phospholmager can be used to generate an image file that can be used to generate araw data set. Depending on the nature of label chosen for labelling the target molecules, other instruments can also be used, for example, when fluorescence is used for labelling, a Fluorolmager can be used to generate an image file from the hybridised target molecules.

The raw data corresponding to mean intensity, median intensity, or volume of the signals in each spot can be acquired from the image file using commercially available software for image analysis. However, the acquired data needs to be corrected for background signals and normalized prior to analysis, since, several factors can affect the quality and quantity of the hybridising signals. For example, variations in the quality and quantity of mRNA isolated from sample to sample, subtle variations in the efficiency of labelling target molecules during each reaction, and variations in the amount of unspecific binding between different macroarrays can all contribute to noise in the acquired data set that must be corrected for prior to analysis.

Background correction can be performed in several ways. The lowest pixel intensity within a spot can be used for background subtraction or the mean or median of the line of pixels around the spots’ outline can be used for the purpose. One can also define an area representing the background intensity based on the signals generated from negative controls and use the average intensity of this area for background subtraction.

The background corrected data can then be transformed for stabilizing the variance in the data structure and normalized for the differences in probe intensity. Several transformation techniques have been described in the literature and a brief overview can be found in Cui, Kerr and Churchill http://www jax.org/research/ churchill/research/ expression/Cui-Transform. pdf).

Normalization can be performed by dividing the intensity of each spot with the collective intensity, average intensity or median intensity of all the spots ina macroarray or a group of spots in a macroarray in order to obtain the relative intensity of signals hybridising to immobilised probes in a macroarray. Several. methods have been described for normalizing gene expression data (Richmond and Somerville, 2000, Current Opin. Plant Biol., 3, p108-116; Finkelstein et al., 2001, In "Methods of Microarray Data Analysis. Papers from CAMDA, Eds.

Lin & Johnsom, Kluwer Academic, p57-68; Yang et al., 2001, In "Optical

Technologies and Informatics”, Bds. Bittner, Chen, Dorsel & Dougherty,

Proceedings of SPIE, 4266, p141-1 52; Dudoit et al, 2000, J. Am. Stat. Ass., 97, p77-87; Alter et al 2000, supra; Newton et al., 2001, J. Comp. Biol, 8, p37-52).

Generally, a scaling factor or function is first calculated to correct the intensity effect and then used for normalising the intensities. The use of external controls has also been suggested for improved normalization.

One other major challenge encountered in large-scale gene expression analysis is that of standardization of data collected from experiments performed at different times. We have observed that gene expression data for samples acquired in the same experiment can be efficiently compared following background correction and normalization. However, the data from samples acquired in experiments performed at different times requires further standardization prior to analysis. This is because subtle differences in experimental parameters between different experiments, for example, differences in the quality and quantity of mRNA extracted at different times, differences in time used for target molecule labelling, hybridization time or exposure time, can affect the measured values. Also, factors such as the nature of the sequence of transcripts under investigation (their GC content) and their amount in relation to the each other determines how they are affected by subtle variations in the experimental processes. They determine, for example, how efficiently first strand cDNAs, corresponding to a particular transcript, are transcribed and labelled during first strand synthesis, or how efficiently the corresponding labelled target molecules bind to their complementary sequences during hybridization. Batch to batch difference in the printing process is also a major factor for variation in the generated expression data.

Failure to properly address and rectify for these influences leads to situations where the differences between the experimental series may overshadow the main information of interest contained in the gene expression data set, i.e. the differences within the combined data from the different experimental series. Hence, when required the expression data should be batch- adjusted prior to data analysis.

Monitoring the expression of a large number of genes in several samples leads to the generation of a large amount of data that is too complex to be easily interpreted. Several unsupervised and supervised multivariate data analysis techniques have already been shown to be useful in extracting meaningful biological information from these large data sets. Cluster analysis is by far the most commonly used technique for gene expression analysis, and has been performed to identify genes that are regulated in a similar manner, and or identifying new/unknown tumour classes using gene expression profiles (Eisen et al., 1998, PNAS, 95, p14863-14868, Alizadeh et al. 2000, supra, Perou et al. 2000, Nature, 406, p747-752; Ross et al, 2000, Nature Genetics, 24(3), p227- 235; Herwig et al., 1999, Genome Res., 9, p1093-1105; Tamayo et al, 1999,

Science, PNAS, 96, p2907-2912). 1n the clustering method, genes are grouped into functional categoties

1999, Tech report Univ Stanford.) Plaid model (Lazzeroni, 2002, Stat. Sinica, 12, p61-86), and self-organizing maps (Tamayo et al. 1999, supra). Also, related methods of multivariate statistical analysis, such as those using the singular value decomposition (Alter et al., 2000, PNAS, 97(18), p10101-10106; Ross et al. 2000, supra) or multidimensional scaling can be effective at reducing the dimensions of the objects under study.

However, methods such as cluster analysis and singular value decomposition are purely exploratory and only provide a broad overview of the internal structure present in the data. They are unsupervised approaches in which the available information concerning the nature of the class under investigation is not used in the analysis. Often, the nature of the biological perturbation to which a particular sample has been subjected is known. For example, it is sometimes known whether the sample whose gene expression pattern is being analysed derives from a diseased or healthy individual. In such instances, discriminant analysis can be used for classifying samples into various groups based on their gene expression data.

In such an analysis one builds the classifier by training the data that is capable of discriminating between member and non-members of a given class.

The trained classifier can then be used to predict the class of unknown samples.

Examples of discrimination methods that have been described in the literature include Support Vector Machines (Brown et al, 2000, PNAS, 97, p262-267),

Nearest Neighbour (Dudoit et al., 2000, supra), Classification trees (Dudoit et al., 2000, supra), Voted classification (Dudoit et al., 2000, supra), Weighted

Gene voting (Golub et al. 1999, supra), and Bayesian classification (Keller et al. 2000, Tec report Univ of Washington). Also a technique in which PLS (Partial

Least Square) regression analysis is first used to reduce the dimensions in the gene expression data set followed by classification using logistic discriminant analysis and quadratic discriminant analysis (LD and QDA) has recently been described (Nguyen & Rocke, 2002, Bioinformatics, 18, p39-50 and 1216-1226).

A challenge that gene expression data poses to classical discriminatory methods is that the number of genes whose expression are being analysed is very large compared to the number of samples being analysed. However in most cases only a small fraction of these genes are informative in discriminant analysis problems. Moreover, there is a danger that the noise from irrelevant genes can mask or distort the information from the informative genes. Several methods have been suggested in literature to identify and select genes that are informative in microarray studies, for example, t-statistics (Dudoit et al, 2002, J.

Am. Stat. Ass., 97, p77-87), analysis of variance (Kerr et al., 2000, PNAS, 98, p8961-8965), Neighbourhood analysis (Golub et al, 1999, supra), Ratio of between groups to within groups sum of squares (Dudoit et al., 2002, supra),

Non parametric scoring (Park et al., 2002, Pacific Symposium on Biocomputing, p52-63) and Likelihood selection (Keller et al., 2000, supra).

In the methods described herein the gene expression data that has been normalized and standardized is analysed by using Partial Least Squares

Regression (PLSR). Although PLSR is primarily a method used for regression analysis of continuous data (see Appendix A), it can also be utilized as a method for model building and discriminant analysis using a dummy response matrix based on a binary coding. The class assignment is based on a simple dichotomous distinction such as breast cancer (class 1) / healthy (class 2),o0ra multiple distinction based on multiple disease diagnosis such as breast cancer (class 1) / ovarian cancer (class 2) / healthy (class 3). The list of diseases for classification can be increased depending upon the samples available corresponding to other cancers or stages thereof.

PLSR applied as a classification method is referred to as PLS-DA (DA standing for Discriminant analysis). PLS-DA is an extension of the PLSR algorithm in which the Y-matrix is a dummy matrix containing zn YOws (corresponding to the number of samples) and K columas (corresponding to the number of classes). The Y-matrix is constructed by inserting 1 in the kth column and -1 in all the other columns if the corresponding ith object of X belongs to class k. By regressing Y onto X, classification of a new sample is achieved by selecting the group corresponding to the largest component of the fitted, x)= (0 1(%), § 2X);--» F(x). Thus, in a-1/1 response matrix, a prediction value below 0 means that the sample belongs to the class designated as -1, while a prediction value above 0 implies that the sample belongs to the class designated as 1.

An advantage of PLSR-DA is that the results obtained can be easily represented in the form of two different plots, the score and loading plots. Score plots represent a projection of the samples onto the principal components and shows the distribution of the samples in the classification model and their relationship to one another. Loading plots display correlations between the variables present in the data set.

It is usually recommended to use PLS-DA as a starting point for the classification problem due to its ability to handle collinear data, and the property of PLSR as a dimension reduction technique. Once this purpose has been satisfied, it is possible to use other methods such as Linear discriminant analysis,

LDA, that has been shown to be effective in extracting further information,

Indahl et al. (1999, Chem. and Intell. Lab. Syst., 49, p19-31). This approach is based on first decomposing the data using PLS-DA, and then using the scores vectors (instead of the original variables) as input to LDA. Further details on

LDA can be found in Duda and Hart (Classification and Scene Analysis, 1973,

Wiley, USA).

The next step following model building is of model validation. This step is considered to be amongst the most important aspects of multivariate analysis, and tests the "goodness" of the calibration model which has been built. In this work, a cross validation approach has been used for validation. In this approach, one or a few samples are kept out in each segment while the model is built using a full cross-validation on the basis of the remaining data. The samples left out are then used for prediction/classification. Repeating the simple cross-validation process several times holding different samples out for each cross-validation leads to a so-called double cross-validation procedure. This approach has been shown to work well with a limited amount of data, as is the case in some of the

Examples described here. Also, since the cross validation step is repeated several times the dangers of model bias and overfitting are reduced.

Once a calibration model has been built and validated, genes exhibiting an expression pattem that is most relevant for describing the desired information in the model can be selected by techniques described in the prior art for variable selection, as mentioned elsewhere. Variable selection will help in reducing the final model complexity, provide a parsimonious model, and thus lead to a reliable model that can be used for prediction. Moreover, use of fewer genes for the purpose of providing diagnosis will reduce the cost of the diagnostic product.

In this way informative probes which would bind to the genes of relevance may- be identified.

We have found that after a calibration model has been built, statistical techniques like Jackknife (Effron, 1982, The Jackknife, the Bootstrap and other resampling plans. Society for Industrial and Applied mathematics, Philadelphia,

USA), based on resampling methodology, can be efficiently used to select or confirm significant variables (informative probes). The approximate uncertainty variance of the PLS regression coefficients B can be estimated by:

M

S?B = % (B-Bn)2)’ m=1 where

S2B = estimated uncertainty variance of B;

B = the regression coefficient at the cross validated rank A using all the N objects;

B,, = the regression coefficient at the rank A using all objects except the object(s) left out in cross validation segment m; and g = scaling coefficient (here: g=1).

In our approach, Jackknife has been implemented together with cross-validation. For each variable the difference between the B-coefficients B; in a cross-validated sub-model and Biot for the total model is first calculated.

The sum of the squares of the differences is then calculated in all sub-models to obtain an expression of the variance of the B; estimate for a variable. The significance of the estimate of By is calculated using the t-test. Thus, the resulting regression coefficients can be presented with uncertainty limits that correspond to 2 Standard Deviations, and from that significant variables are detected.

No further details as to the implementation or use of this step are provided here since this has been implemented in commercially available software, The Unscrambler, CAMO ASA, Norway. Also, details on variable selection using Jackknife can be found in Westad & Martens (2000, J. Near Inf.

Spectr., 8, p117-124).

The following approach can be used to select informative probes from a gene expression data set: a) keep out one unique sample (including its repetitions if present in the data set) per cross validation segment, b) build a calibration model (cross validated segment) on the remaining samples using PLSR-DA; c) select the significant genes for the model in step b) using the

Jackknife criterion; d) repeat the above 3 steps until all the unique samples in the data . set are kept out once (as described in step a). For example, if 75 unique samples are present in the data set, 75 different calibration models are built resulting in a collection of 75 different sets of significant probes; e) select the most significant variables using the frequency of occurrence criterion in the generated sets of significant probes in step d). For example, a set of probes appearing in all sets (100%) are more informative than probes appearing in only 50% of the generated sets in step d).

Once the informative probes for a disease have been selected, a final model is made and validated. The two most commonly used ways of validating the model are cross-validation (CV) and test set validation. In cross-validation, the data is divided into k subsets. The model is then trained k times, each time leaving out one of the subsets from training, but using only the omitted subset to compute error criterion, RMSEP (Root Mean Square Error of Prediction). Ifk equals the sample size, this is called "leave-one-out" cross-validation. The idea of leaving one or a few samples out per validation segment is valid only in cases where the covariance between the various experiments is zero. Thus, one sample at-a-time approach can not be justified in situations containing replicates since keeping only one of the replicates out will introduce a systematic bias in our analysis. The correct approach in this case will be to leave out all replicates of the same samples at a time since that would satisfy assumptions of zero covariance between the CV-segments.

The second approach for model validation is to use a separate test-set for validating the calibration model. This requires running a separate set of experiments to be used as a test set. This is the preferred approach given that real test data are available.

The final model is then used to identify a cancer or stage thereof in test samples. For this purpose, expression data of selected informative genes is generated from test samples and then the final model is used to determine whether a sample belongs to a diseased or non-diseased class or has a cancer or stage thereof.

Preferably a model for classification purposes is generated by using the data relating to the probes identified according to the above described method.

Preferably the sample is as described previously. Preferably the oligonucleotides which are ;mmobilized in step (a) are randomly selected from within the family described hereinbefore, but alternatively may be selected to represent the different families, e.g. by selecting one or more of the oligonucleotides corresponding to genes encoding proteins with common functions in different families. Bspecially preferably, said selection is made to encompass oligonucleotides derived from genes of family (i) and (ii). Such oligonucleotides may be of considerable length, e.g. if using CDNA (which is encompassed within the scope of the term "oligonucleotide"). The identification of such cDNA molecules as useful probes allows the development of shorter oligonucleotides which reflect the specificity of the cDNA molecules but are easier to manufacture and manipulate.

The above described model may then be used to generate and analyse data of test samples and thus may be used for the diagnostic methods of the invention. In such methods the data generated from the test sample provides the gene expression data set and this is normalized and standardized as described above. This is then fitted to the calibration model described above to provide classification.

The method described herein can also be used to simultaneously select informative probes for several cancers. Depending upon which cancers have been included in the calibration or training set, informative probes can be selected for the said cancers. The informative probes selected for one cancer may or may not be similar to the informative probes selected for another cancer of interest. It is the pattern with which the selected genes are expressed in relation to each other during a cancer or stage thereof, that determines whether or not they are informative for the cancer or stage thereof.

In other words, informative genes are selected based on how their expression correlates with the expression of other selected informative genes under the influence of responses generated by the cancer or stage thereof under investigation.

For the purpose of isolating informative probes or identifying several cancers and stages thereof simultaneously, the gene expression data set must contain the information on how genes are expressed when the subject has a particular cancer or stage thereof under investigation. The data set is generated from a set of healthy or diseased samples, where a particular sample may contain the information of only one cancer or stage thereof or may also contain information about multiple cancers or stages thereof. Hence, the method also teaches an efficient experimental design to reduce the number of samples required for isolating informative probes by selecting samples representing more than one cancer or stage thereof.

As mentioned previously, in view of the high information content of most transcripts, the identification and selection of informative probes for use in diagnosing, monitoring or identifying a particular cancer or stage thereof may be dramatically simplified. Thus the pool of genes from which a selection may be made to identify informative probes may be radically reduced.

Unlike, in prior art technologies where informative probes are selected from a population of thousands of genes that are being expressed in a cell, like in microarray, in the method described herein, the informative probes are selected from 2 limited number of genes as described in the gene sequence families described hereinbefore. From within these families, probes of interest may be randomly selected.

Thus in a preferred aspect, said set of oligonucleotides are randomly 3 selected from the primary oligonucleotides as described hereinbefore.

As referred to herein "random" refers to selection which is not biased based on the extent of information carried by the transcripts in relation to the cancer or organism under study, ie. without bias towards their likely utility as informative probes. Whilst a random selection may be made from a pool of transcripts (or related products) which have been biased, e.g. to highly or moderately expressed transcripts, preferably random selection is made from a pool of transcripts not biased or selected by a sequence-based criterion. The larger set may therefore contain oligonucleotides corresponding to highly and moderately expressed genes, or alternatively, may be enriched for those corresponding to the highly and moderately expressed genes.

Random selection from highly and moderately expressed genes can be achieved in a wide variety of ways. For example, by randomly picking a significant number of cDNA clones from a cDNA library constructed from a biological specimen under investigation containing clones corresponding to the gene sequence families described hereinbefore. Since, in a cDNA library, the cDNA clones corresponding to transcripts present in high or moderate amount are more frequently present than transcripts corresponding to cDNA present in low amount, the former will tend to be picked up more frequently than the latter.

A pool of cDNA enriched for those corresponding to highly and moderately expressed genes can be isolated by this approach.

To identify genes that are expressed in high or moderate amount among the isolated population for use in methods of the invention, the information about the relative level of their transcripts in samples of interest can be generated using several prior art techniques. Both non-sequence based methods, such as differential display or RNA fingerprinting, and sequence-based methods such as microarrays or acroarrays can be used for the purpose. Alternatively, specific primer sequences for highly and moderately expressed genes can be designed and methods such as quantitative RT-PCR can be used to determine the levels of highly and moderately expressed genes. Hence, a skilled practitioner may use a variety of techniques which are known in the art for determining the relative level of mRNA in a biological sample.

Especially preferably the sample for the isolation of mRNA in the above described method is as described previously and is preferably not from the site . of disease and the cells in said sample are not disease cells and have not contacted disease cells, for example the use of a peripheral blood sample for detection of non-haematopoietic cancer, €.8. breast cancer.

The following examples are given by way of illustration only in which the Figures referred to are as follows:

Figure 1 shows the possible interplay of various factors responsible for changes in expression in an individual with breast cancer,

Figure 2 shows the projection of 102 normal (including benign) and breast cancer samples onto a classification model generated by PLSR-DA using the data of 35 informative genes, in which PC is the principal components and N and C are normal and breast cancer samples, respectively;

Figure 3 shows a prediction plot based on 3 principal components using the data of 35 cDNAs; and

Figure 4 shows the mean level of expression of the 35 genes used for prediction of breast cancer.

Example 1: Diagnosis of Breast Cancer

Methods

Blood samples

Blood samples were collected from donors with their informed consent under an approval from Regional Ethical Committee of Norway. All donors were treated anonymously during analysis. Blood was drawn from females with a suspect initial mammogram, which included both females with breast cancer and fernales with abnormal mammograms, prior to any knowledge of whether the

~ WO 2005/118851 PCT/GB2005/002180 abnormality observed during first screening was benign or malignant. In all cases, the blood samples were drawn between 8 am. and 4 p.m. From each female, 10 ml blood was drawn by skilled personnel either in vacutainer tubes containing EDTA as anticoagulant (Becton Dickinson, Baltimore, USA) or directly in PAXgene ™ tubes (PreAnalytiX, Hombrechtikon, Switzerland).

Blood collected in EDTA tubes was immediately stored at - 80°C, while PAX tubes were left overnight and then stored at - 80°C until used.

Preparation of cDNA arrays 1435 cDNA clones were randomly picked froma plasmid library constructed from whole blood of 550 healthy individuals (Clontech, Palo Alto, USA). About 20% of the randomly picked clones were redundant. For amplification of inserts, bacterial clones were grown in microtiter plates containing 150 wl LB with 50 pg/ml carbenicillin, and incubated overnight with agitation at 37°C. To lyse the cells, 5 pl of each culture were diluted with 50 pl H,O and incubated for 12 min. at 95°C. Of this mixture, 2 pl were subjected to a PCR reaction using 40 ppmol of 5'- and 3'- sequencing primer in the presence of 1.5 mM MgCl. PCR reactions were performed with the following cycling protocol: 4 min. at 95°C, followed by 25 cycles of 1 min. at 94°C, 1 min. at 60°C and 3 min. at 72°C either in a RoboCycler® Temperature Cycler (Stratagene, La Jolla, USA) or

DNA Engine Dyad Peltier Thermal Cycler (MJ Research Inc., Waltham, USA).

The amplified products were denatured with NaOH (0.2 M, final concentration) for 30 min and spotted onto Hybond-N" membranes (Amersham Pharmacia

Biotech, Little Chalfont, UK), using a MicroGrid II workstation according to the manufacturer’s instructions (BioRobotics Ltd, Cambridge England). The immobilized cDNAs were fixed using aUV cross-linker (Hoefer Scientific

Instruments, San Francisco, USA).

In addition to the 1435 cDNAs, the printed arrays also contained controls for assessing background level, consistency and sensitivity of the assay. These were spotted at multiple positions and included controls such as PCR mix (without any insert), controls of SpotReport™ 10 array validation system

(Stratagene, La Jolla, USA) and cDNAs corresponding to constitutively expressed genes such as B-actin, y-actin, GAPDH, HOD and cyclophilin.

RNA extraction, probe synthesis and hybridization

Blood collected in EDTA tubes was thawed at 37°C and transferred to PAX tubes, and total RNA was purified according to the supplier’s instructions (PreAnalytiX, Hombrechtikon, Switzerland). From blood collected directly in

PAX tubes total RNA was extracted in the tubes as above without any transfer to new tubes. Contaminating DNA was removed from the isolated RNA by

DNAase I treatment using DNA-free kit (Ambion, Inc. Austin, USA). RNA quality was determined visually by inspecting the integrity of 28S and 18S ribosomal bands following agarose gel electrophoresis. Only samples from which good quality RNA was extracted were used in this study. In our experience, blood collected in EDTA tubes often resulted in poor quality RNA, while that collected in PAX tubes almost always gave good quality RNA. The concentration and purity of extracted RNA was determined by measuring the absorbance at 260 nm and 280 nm. From the total RNA, mRNA was isolated using Dynabeads according to the supplier’s instructions (Dynal AS , Oslo,

Norway).

Labelling and hybridization experiments were performed in 16 batches.

The number of samples assayed in each batch varied from six to nine. To minimize the noise due to batch-to-batch variation in printing, only the arrays manufactured during the same print run were used in each batch. When samples were assayed more than once (replicates), aliquots from the same mRNA pool were used for probe synthesis. For probe synthesis, aliquots of mRNA corresponding to 4-5 pg of total RNA were mixed together with oligodTzsnv (0.5 pg/pl) and mRNA spikes of SpotReport™ 10 array validation system (10 pe; Spike 2, 1 pg), heated to 70°C, and then chilled on ice. Probes were prepared in 35 pl reaction mixes by reverse transcription in the presence of 50 pCi [oP] 320 dATP, 3.5 uM dATP, 0.6 mM each of dCTP, dTTP, dGTP, 200 units of

SuperScript reverse transcriptase (Invitrogen, LifeTechnologies) and 0.1 M DTT labelling for 1.5 br at 42°C. Following synthesis, the enzyme was deactivated for

10 min. at 70°C and mRNA removed by incubating the reaction mix for 20 min. at 37°C in 4 units of Ribo H (Promega, Madison USA). Unincorporated nucleotides were removed using ProbeQuant G 50 Columns (Amersham

Biosciences, Piscataway, USA).

The membranes were equilibrated in 4 x SSC for 2 hr at room temperature and prehybridized overnight at 65°C in 10 ml prehybridization solution (4 x SSC,0.1 M NaH;PO4 1 mM EDTA, 8% dextran sulphate, 10 x

Denhardt's solution, 1% SDS). Freshly prepared probes were added to 5 ml of the same prehybridization solution, and hybridization continued overnight at 65°C. The membranes were washed at 65°C with increasing stringency (2 x 30 min. each in 2 x SSC, 0.1% SDS; 1x SSC, 0.1% SDS; 0.1 x SSC, 0.1% SDS).

Quantification of hybridization signals

The hybridized membranes were exposed to Phosphoscreen (super resolution) for two days and an image file generated using Phospholmager (Cyclone,

Packard, Meriden, USA). The identification and quantification of the hybridization signals, as well as subtraction of local background values was performed using Phoretix software (Non Linear Dynamics, UK). For background subtraction, the median of the line of pixels around each spot outline was subtracted from the intensity of the signals assessed in each spot.

Data analysis

From the 1435 background-subtracted expression data, signals of 67 genes were removed from each membrane to exclude genes expressed with a high degree of variance. These included removal of 1.25% of the lowest and highest signals from each membrane. For normalization, the value of each spot was first divided by the mean of signals in each array followed by a cube root transformation of all the spots. The normalized data was then batch adjusted using a one-way analysis of variance (ANOVA).

The pre-processed data was then used to isolate the informative probes by:

a) buildinga crossvalidated PLSR model, where one unique sample (including all repetitions of the selected sample) was kept out per cross- validation segment. ‘5 b) selecting the set of significant genes for the model in step a) using the

Jackknife criterion. ¢) buildinga crossvalidated PLSR-DA model as in step a) using the gene selected in step b). . d) selecting again the set of most significant genes for the model in step c) using the Jackknife criterion.

Step b) resulted in 125 genes.

Step d) resulted in selection of 35 significant genes. Based on these genes a final classification model was constructed.

The selected informative probes based on occurrence criterion were used to construct a classification model. The result of the classification model based on 35 probes is shown in Figure 2 in which it is seen that the expression pattern of these genes was able to classify most women with breast cancer and women with no breast cancer into distinct groups. In this figure PC1 and PC2 indicate the two principal components statistically derived from the data which best define the systemic variability present in the data. This allows each sample, and the data from each of the informative probes to which the sample's labelled first strand cDNA was bound, to be represented on the classification model as a single point which is a projection of the sample onto the principal components - the score plot.

Figure 3 shows the prediction plot using the 35 significant genes. In the prediction plot shown, the cancer samples appear on the x axis at +1 and the non-cancer samples appear at -1. The y axis represents the predicted class membership. During prediction, if the prediction is correct, cancer samples should fall above zero and non-cancer samples should fall below zero. In each case almost all samples are correctly predicted. For cross-validation 102 experimental samples were divided into 60 cross-validation segments where each segment represented one unique sample and included its replicates if present. :

Correct prediction of most breast cancer cells was achieved. 19 out of 22 cancer patients were correctly predicted as were 34/35 normal patients. Full details of the individuals examined and the accuracy of prediction are shown in Table 1.

Table 2 provides details of the 35 informative genes, the genes in public databases to which they show sequence similarity and their putative biological function. Their sequences follow these examples.

Figure 4 shows the expression level of the 35 genes and it will be seen that some are over-expressed and others under-expressed relative to expression in normal : patients.

Example 2: Identification of further informative probes and use in diagnosis of breast cancer

Methods

The methods of identification and analysis used were essentially as described in

Example 1, except that instead of preparing a cDNA array, samples were analysed using a commercially available platform for large-scale gene expression analysis (Agilent 22K chip).

A larger number of samples comprising 122 in total (78 control and 44 with breast cancer) were analysed. The data was analysed using PLSR as described previously. The genes of interest were selected by a 10-fold cross validation approach. Thus, the data from 122 samples was divided into 10 sets, each set containing 12-13 samples. A calibration model was built on 9 sets leaving out one set. Significant genes were identified by the Jackknife technique on the built-in model. These steps were repeated for all 10 sets, in which each set was kept out at least once. Informative genes were then identified based on the frequency of occurrence criterion. 109 genes were found to informative in all 10 calibration models.

Results

The above described 109 genes and 3 further genes were used to predict the classification of 122 of the samples used. The results are shown in the table below. predicted predicted

The 109 informative genes may be divided into three categories, namely those falling into families (i) and (ii) as described herein and other genes. Table 3 provides details of the informative probes whose corresponding genes fall into families (i) and (ii) and provides the number assigned by Agilent to that probe.

Table 4 similarly provides details of the informative probes whose corresponding genes do not appear to fall within families (i) and (ii). Tables 5 and 6 provide details of the genes to which the probes in Tables 3 and 4, respectively, show sequence similarity, their putative biological function where known and the accession numbers for those genes.

Appendix A

Partial Least Squares regression (PLSR)

Let a multivariate regression model be defined as:

Y=XB+F where .

X a NxP matrix with N predictor variables (genes);

Y (NxJ) being the J predicted variables. In our case Y represents a matrix containing dummy variables;

B is a matrix of regression coefficients; and

F is a NxJ matrix of residuals. i5

The structure of the PLSR model can be written as:

X = TP" + Ba, and

Y= TQ" + Fa, where where

T (Nx4) is a matrix of score vectors which are linear combinations of the x-variables;

P (PxA) is a matrix with the x-loading vectors pa as columns;

Q (JxA) is a matrix with the y-loading vectors Ge as columns;

E, (NxP) is the matrix for X after A factors; and

F, (NJ) is the matrix for Y after A factors.

The criterion in PLSR is to maximize the explained covariance of [X,Y]. This is achieved by the loading weights vector Was, which is the first eigenvector of

E."F.Fa'E, (B. 2nd Fa are the deflated X and Y after a factors or PLS components).

The regression coefficients are given by:

B — wETw)'Qt

A PLSR model with full rank, i.e. maximum number of components, is

S equivalent to the MLR solutions. Further details on PLSR can be found in

Marteus & Naes, 1989, Multivariate Calibration, John Wiley & Sons, Inc., USA and Kowalski & Seasholtz, 1991, supra.

Nucleotide Sequences of 34/35 genes selected by Jacklanife

Clone Ids and their sequences 1-30

CTTTTCCTCCCGCT GTCOCCCACGGAGGGGACTGCICT CCCCOGCTGCATCCIT

TCTGTGAGGTACCTT. ACCCACCT CAGCACCTGAGAGGGTGAAATAGAATTCT AA

CCT CGACATTCGGGAAGTGTITTTTGAGAAGTCT OGGTCGGTAAGGGAAGTCTTC

CAAGTCCGTGCAGCACT AACGTATTGGCACCTGCCTCCICIT CGGCCACCCCCC

AGATGAGGCAGCTGTGACT GTGTCAAGGGAAGCCACGACTCT GACCATAGTCTT

CTCTCAGCTTCCACTGCCGTCT CCACAGGAAACCCAGAAGTTCT GTGAACAAGT

CCATGCTGCCATCAAGGCAT TTATTGCAGTGTACTATTTGCT TCCAAAGGATCA

GGCCCTGAGAACAATGACCT TATTTCCTACAACAGTGTCT GGGTTGCGTGCCAG

CAGATGCCT CAGATACCAAGAGATAACAAAGCT GCAGCTCTTTTGATGCTGACC

AAGAATGTGGATTTTGTGAAGGATGCACATGAAGAAATGGAGCAGGCT GTGGAA

GAATGTGACCCTTACTCTGGCCT CTTGAATGATACTGAGGAGAACAACT CTGAC

AACCACAATCATGAGGATGATGTGTTGGGGTTTCCCAGCAATCAGGACTTGTAT

TGGTCAGAGGACGATCAAGAGCTCATAATCCCATGCCTT GCGCTGGTGAGAGCA

TCCAAAGCCTGCCTGAAGAAAATT CGGATGTT AGTGGCAGAGAATGGGAAGAAG

GATCAGGTGGCACAGCT GGATGACATTIGTGGATATTTCT GATGAAATCAGCCCT

AGTGTGGATGATTTGGCTCT GAGCATATATCCACCT ATGTGTCACCTGACCGTG

CGAATCAATTCTGCGAAACTT GTATCTGTTTTAAAGAAGGCACTT GAAATTACA

AAAGCAAGTCATGTGACCCCTICAG CCAGAAGATAGTTGGATCCCT TTACITATT

AATGCCATTGATCATTGCATGAATAGAATCAAGGAGCT CACTCAGAGTGAACTT

GAATTATGACTTTTCAGGCT CATTTGTACTCTCTTCCCCTCT CATCGTCATGGT

CAGGCTCTGATACCTGCTTTTAAAATGGAGCT AGAATGCTTGCTGGATTGAAAG

GGAGTGCCTATCT ATATTTAGCAAGAGACACT ATTACCAAAGATTGTTGGTTAG

GCCAGATTGACACCT ATTTATAAACCATATGCGTATATTTTTCTGTGCT ATATA

TGAAAAATAATTGCATGATTTCTCATTCCT GAGTCATTTCTCAGAGATTCCTAG

GAAAGCTGCCTTATTCICTT TTTGCAGTAAAGTATGTTGTTTTCATTGTAAAGA

TGTTGATGGTCTCAATAAAATGCTAACTT GCCAGTGAAAAAAAAAAAAAA

111-02

AGGATCTAAGACCAGCCT GGCAGCCACCAGATGGTGATTCTAGTCCT GGCTCAG

TCAGTAATAGGTCACT GACCCCAGAGAAATCAATTCAGCCTCCCCAGGTCCTTG

GATTTCTTTCT GTGAAAATGAAAGCATAGGTAGGAATTTCCCATGGAACAGCTA

GCAGAGGAGAAATATTAAAAGT CAGGAGACTCATGCTATAGTTT TCATACTTCA

TTACAACAATGTTGTTTAGGACAAGTGAGTTAACCT GTTAGCTTCCICTATATA

AAATGGAAAGTCATTAAAAACCT. ACATAGCAGGGTTCT TGTGAAGATCAAGTGA

TAATGTAGGAAGCATGTACAAATGTCACATT CTGCCGTCACGTAATGGTCCT CA

CAGCTTGAGGT AGCATTTAGCATGTGTCATGATTTAGT ACAAGGGTTGGCAAAC

S TGTTGCTCTTGGATTAAGTCTGGCT CATTGCCT GTTTTTCAAAGAAAAAAATTG

TATATGTGTGTATATATGTTATATATAGGTACACACACATATGTGCT ATATATA

GCATATATACACACATAATATAT AAACATGTACATATATAGCATTATATATATA

CGTGTATAATATCTCCAGTCCT CATGACCAGCCATGCTT GTTCATTTACATTTG

CATACTCTATGATTGCTTTCAT GCAACAATGGCAGAGTTGAGTGATTGTTITGC

AACAGAGACTGTATGGCCCACT. AAACCT AAAATATTTAGTCTCTGACCCTGAAA

TGTAAGATTGAT AGCCCAGGACCAGGCGTGGTGGCT CACACTTGTAATCCTAGC

ACT TTGGCAGGCCAAGGAGGGTGGATCACCT GAGGTCAGGAGTTAAAGACCAGC

CTGGCCAACATGGTGAAACCCTGACTCT ACTAAAAATACAGAAATTAGCTGGGC

GTGGTAATGGGTGCCTGCAATCCAAGCT ACTCTGGAGGCTGAGGCAGGAGAATC

ACTT! GAACCCAGGAGGCAGAAGTTACAGTGAGCT GAGATGGTGCCACTGCACTC

CAGCCTGGACGACAGAGTGAGACT CCATCTCAAAAA m-27

CCATTCTCCTGCCTCAGCCTCTCAAGTAGCT GGGACTACAGGCGCCCACAACCA

CGCCOGGCTAATGTTTTTGGTATTTIT CGTAGAGACGGGGTTTCACCTTGTTAG

CCAGGATGGTCTTGATCTCCTGACCTCGTGATCT GCCTGCCTCGGCCTCCCAAA

GTGTTGGGATTACAGGCACATTTTTCACAATTTTTTAACACTT AAGAATGACTT

AACTGAATCATGCCTTTAGAAGAAACTTTCT GTTTAAAAAAAAAAAAAAA

11-60

CTGCOGCOGCCCCCAGCTCCCCOGCCT CGGGGAGGGCACCAGGTCACTGCAGCC

AGAGGGGT CCAGAAGAGAGAGGAGGCACTGCCT CCACTACAGCAACTGCACCCA

CGATGCAGAGCATCAAGT GCGTGGTGGTGGGTGATGGGGCT GTGGGCAAGACGT

GCCTGCT CATCTGCTACACAACTAACGCTTTCCCCAAAGAGTACATCCCCACCG

TGTTCGACAATTACAGCGCGCAGAGCGCAGTTGACGGGCGCACAGTGAACCT GA

ACCT GTGGGACACTGCGGGCCAGGAGGAGTATGACCGCCT CCGTACACTCTCCY

ACCCTCAGACCAACGTTTTCGTCATCTGTTTCT! CCATTGCCAGTCOGCCGTCCT

ATGAGAACGTGCGGCACAAGTGGCAT CCAGAGGTGTGCCACCACTGCCCTGATG

TGCCCATCCTGCT GGTGGGCACCAAGAAGGACCT GAGAGCCCAGCCTGACACCC

TACGGCGCOCTCAAGGAGCAGGGCCAGGCGCCCATCACACCGCAGCAGGGCCAGE

CACTGGCCAAGCAGATCCACGCTGTGCGCTACCT CGAATGCTCAGCCCTGCAAC

AGGATGGTGTCAAGGAAGTGTTCGCCGAGGCT GTCCGGGCTGTGCTCAACCCCA

CG CCGATCAAGOGTGGGCGGTCCT GCATCCTCTTGT GACCCTGGCACTTGGCTT

GGAGGCTGCCCCIGCCCT CCCCCCACCAGTTGTGCCTTG GTGCCTTGTCCGCCT

CAGCTGTGCCTTAAGGACT AATTCTGGCACCCCTTT CCAGGGGGTTCCCTGAAT

GCCTTTTTCTICTGAGTGCCT TTTTCTCCTTAAGGAGGCCT GCAGAGAAAGGGGC

TTTGGGCTCIGCCCCCCTCTGCTT GGGAACACTGGGTATTCT CATGAGCTCATC

CAAGCCAAGGTTGGACCCCT CCCCAAGAGGCCAACCCAGTG CCCCCTCCCATTT

TCCGTACTGACCAGTTCATCCAGCTT TCCACACAGTTGTTGCTGCCT. ATTGTGG

TGCCGCCTCAGGTTAGGGGCICT CAGCCATCTCTAACCTCTGCCCT CGCTGCTC

TTGGAATTGCGCCCCCAAGATGCTCT CTCCCTTCT CCAATGAGGGAGCCACAGA

ATCCTGAGAAGGTGAATGTGCCCT AACCTGCTCCTICT GTGCCTAGGCCTTACGC

ATTTGCTGACTGACT CAGCCCCCATGCTTCTGGGGACCTT TCCTACCCCCATCA

GCATCAATAAAACCTCCTGTCT CCAGTGA

IvV-26

CAGCCCTCCGTCACCT CTTCACCGCACCCTCGGACT GCCOCCAAGGCCCCCGCOG

CCGCT CCAGOGCCGOGCAGCCACCGCCGCCGCCGCCGCCT CTCCTTAGTCOGCCG

CCATGACGACCGCGTCCACCT CGCAGGTGCGCCAGAACTACCACCAGGACT CAG

: AGGCCGCCATCAACOGCCAGATCAACCTGGAGCT CTACGCCTCCTACGTTTACC

TGTCCATGTCTTACTACT TTGACCGCGATGATGTGGCTTTGAAGAACTT TGCCA

AATACTTTCITCACCAATCT CATGAGGAGAGGGAACATGCT! GAGAAACTGATGA

AGCTGCAGAACCAACGAGGTGGCCGAATCTT CCTTCAGGATATCAAGAAACCAG

ACTGTGATGACTGGGAGAGCGGGCTGAATGCAATGGAGTGTGCATTACATTIGG

AAAAAAATGTGAATCAGTCACTACT GGAACTGCACAAACTGGCCACTGACAAAA

ATGACCCCCATTTGTGTGACTT CATTGAGACACATTACCT GAATGAGCAGGTGA

AAGCCATCAAAGAATTGGGTGACCACGTGACCAACT TGCGCAAGATGGGAGCGC

COGAATCTGGCTTGGCGGAATATCTCT TTGACAAGCACACCCTGGGAGACAGTG

ATAATGAAAGCTAAGCCTCGGGCT AATTTCCOCATAGCCOGTGGGGTGACTTCCC

TGGTCACCAAGGCAGTGCATGCAT GTTGGGGTTTCCTTTACCTTITCTATAAGT

TGTACCAAAACATCCACTTAAGTTCT TTGATTTGTACCATTCCTTCAAATAAAG

AAATTTGGTACCCAAAAAAAA v-41

GCCATTTCTAAGACCTACAGCT ACCTGACCCCOGACCTCTGGAAGGAGACT GTA

TTCACCAAGTCTCCCTAT CAGGAGTTCACTGACCACCT CGTCAAGACCCACACC

AGAGTCTCCGTGCAGCGGACTCAGGCT CCAGCTGTGGCTACAACATAGGGTTTT

TATACAAGAAAAATAAAGTG AATTAAGCGTGAAAA v-s1

ATTICT GTGGATACAGTGCCCACOGCCCTCCT CCACTTGGAAACGGTATCCTCC

CTGCCCATCCGTCT GTCTGTCGCCCTTCTCCCGGCCCT CACTAAGCCCCGGCAC

TTCTAGTGGTCTCACCT GGAGGCAAGAGGGAGGGGACAGAGG CCCTGCCACGTC

CCGCTGCCTCCTGCTCTCT! GGAGGTACT GAGACAGGGTGCT GATGGGAAGGAGG

GGAGCCTTTGGGGGGCCACCCGGGGCCTGGACCT ATGCAGGGAGGCCACGTCCC

ACCCCACCTCT TGTTTCTGGGTCCCTGCTCCCCT TTGGGGGTGTGTGTGTGTGT

TTTAATTTTCT TTATGGAAAAATTGACAAAAAAAAATAGAGAGAGAGGTATTTA

ACTGCAATAAACTGGCCCCATGTGGCCCCCGCCTTGTCAAAAAAAAAA

V-09

TGGATTCCCGTCGTAACTT AAAGGGAAACTT TCACAATGTCCGGAGCCCTTGAT

GTCCTGCAAATGAAGGAGGAGGATGTCCT TAAGTTCCTTGCAGCAGGAACCCAC

TTAGGTGGCACCAATCTTGACTT CCAGATGGAACAGTACATCTAT AAAAGGAAA

AGTGATGGCATCTATAT CATAAATCTCAAGAGGACCTGGGAGAAGCT TCTGCTG

GCAGCTCGTGCAATTGTTGCCATIGAAAACCCTGCTGATGTCAGTGTTATATCC

TCCAGGAATACTGGCCAGAGGGCTGTGCT GAAGTTTGCTGCTGCCACTGGAGCC

ACTCCAATTGCTGGCCGCTTCACTCCT GGAACCTTCACTAACCAGATCCAGGCA

GCCTTCCGGGAGCCACGGCTTCTTGTGGTTACT GACCCCAGGGCTGACCACCAG

CCTCTCACGGAGGCATCTTATGTTAACCTACCT ACCATTGOGCTGTGTAACACA

GATTCTCCTCTGOGCTATGTGGACATT GCCATCCCATGCAACAACAAGGGAGCT

CACTCAGTGGGTTTAATGTGGTGGATGCTGGCT CGGGAAGTTCTGCGCATGCGT

GGCACCATTTCCCGTGAACACCCATGGGAGGTCATGCCT GATCTGTACTTCTAC

AGAGATCCTGAAGAGAT TGAAAAAGAAGAGCAGGCTGCIGCT GAGAAGGCAGTG

ACCAAGGAGGAATTTCAGGGTGAATGGACTGCT CCCGCTCCTGAGTTCACTGCT

ACTCAGCCTGAGGTTGCAGACT GGTCTGAAGGTGTACAGGTGCCCTCTGTGCCT

ATTCAGCAATTCCCTACTGAAGACTGGAGCGCT CAGCCTGCCACGGAAGACTGG

TCTGCAGCTCCCACTGCT CAGGCCACTGAATGGGTAGGAGCAACCACT GACTGG

TCTTAAGCTGTTCTTGCATAGGCTCTTAAGCAGCAT GGAAAAATGGTTGATGGA

AAATAAACATCAGTTTCT

20

V-38

GTTTAAATTTGACAAACTAAAGCTAATTACT GCTATAAGAGTAATAACTGCTCA

TTTTCCATAACT CATTCTTAAAGTTTTAGTAATGTAAAAGTTATTTTITIGCAG

TAAGTTATAATGATAGAAGCTTACATGT TTTTTCATGCCTCATCTGTTTCCCCT

TAAAACTATAATTATCAGTAAAGTCCT GTGGTATTTTTCAATTTGTAAGAAACT

AGGCTATATATACATTGGGAAAAACAGCCTT CATTTGTCAATGCACTAGTGTTC

CAAAGGTTTCTGGTAATTGTGTGCTATTGCTTT TTGTTGACTTGCAAAAAAAAA

AAAAAAAAAATTACTATGACT TGTGGTAGCCCTGCAACCTT CGGAAGTGCTTAG

CCCAGTCT GACCATACATTTATATTTAGAATGCTT AGGTAAATAAATAATATGC

CTAAACCCAATGCT ATAAGATACTATATAATATCT CATAATTTTAAAAATCACT

GTTTTGTATAAT AATAAAACAAGGCAGGCAAGCT GTTCTACAATGACTGTTGGT

AAGGGTGCTGAGGAAGAAAAACAAACAATCTT GATTCAGGGATAGTGAATAGAC

AAAAAATGTCCT AATCAATGAAGCTGTGTGATGATT CTGATTGACAGAGAGTGC

TGCCACAAGATTCTT AGGCTACACTCAAATCAGCAGAAAAAGTGCT ACAATAAA

TTAGAAGTGACT ATTACAGGTGCAGATGAGGGTTGGTAGTACCT GTTTGCCATT

TCTCTTCTAATCTTATATTTICT GACCCTCCT ACTGTAAGTOGOGCGGAGGOGG

AGGCTTGGGTGOGTTCAAGAT TCAACT TCACCOGTAACCCACCGCCATGGCCGA

GGAAGGCATTGCTGCT GGAGGTGTAATGGACGTTAATACTGCT TTACAAGAGGT

TCT GAAGACTGCCCTCATCCACGATGGCCT AGCACGTGGAATTCGCGAAGCTGC

CAAAGCCTT AGACAAGCGCCAAGCCCATCTTT GTGTGCTTGCATCCAACTGTGA

TGAGCCT ATGTATGTCAAGTTGGTGGAGGCCCTT TGTGCTGAACACCAAATCAA

CCTAATT AAGGTTGATGACAACAAGAAACTAGGAGAATGGGTAGGCCTTT GTAA

AATTGACAGAGAGGGGAAACCCCGTAAAGTGGTTGGTTGCAGTTGTGTAGTAGT

TAAGGACTATGGCAAGGAGTCT CAGGCCAAGGATGTCATTGAAGAGTATTTCAA

ATGCAAGAAATGAAGAAATAAATCTTT GGCTCACAAA

VI-44

GAGAATGGCITGAACCCAGTAGGCAGAGGTTGTAGTGAGCCGAGATTGGGCCAC

TGCACTTTAGCCT GGGTGACAGAGTGAGACTCTIGTCT CAAAAAAAAAAAAAAAA

AATTTAAATAAAATAAAAAACCTTTACTT ATTTTTAAATTGGGTTGTCTITTTTG

GTATTGAGTTGTTAAAGTTCTTTATATATTTTAGGTACAAATCCCTT ATGAGAT

ACGTGATTTGAAAATATTTTCTCCCATTCT GTGGGTTGCTTTTTCACTTTCTTG

GTTGTATCCT TTGAAGCACAGAAGTTTTAAATTTTGATGAAGTCCAGTITATTT

ATTTTTTTGCT GTTGTTTCTGCTCATACTTTTGAGGTCATGTCTGAGAAACCAT

TGTCAAATCCAAGGTOGTGATGACTT ACCCCTGTGTTTTCTTCTAAGAGTTTTA

AAGGCATCTGAAGCTTAATGTGCACT. AGATGGATTCTAAATATCATCTCATCCA

AAACCTGCTATATATACTACCTTCCTCATCTCAGTT GAAGGCAAGTCCATTGTT

, TCAATTGCCTGGGCAAAAAATATTCT AAATAATTCATAATTTTTCCTCAACTCC

ACATCTATTGGTAAATCCTGTGGGTTCTCCT TTTAAAACATATCCAAAATAGAA

TCATTTCTCACT ATCATTCCACTGCAGGCACCAAGTCT CAATAGTCTCCTAGCA

GATAATCATGTCT ACATTTATTCTCAATGTAGCAGCTAGAGAGCT TITTTG

VI1-49

GCGGTCGTAAGGGCTGAG GATTTTTGGTCOGCACGCTCCTGCTCCTGACT CACC

GCTGTTCGCTCTCGCOGAGGAACAAGTCGGTCAGGAAGCCOGOGOGCAACAGCC

ATGGCITTTAAGGATACCGGAAAAACACCCGTGGAGCCGGAGGTGGCAATTCAC

CG AATTCGAATCACCCTAACAAGCCGCAACGTAAAATCCTTGGAAAAGGTGTGT

GCTGACTTGATAAGAGGCGCAAAAGAAAAGAATCTCAAAGTGAAAGGACCAGTT

0G AATGOCTACCAAGACTTTGAGAATCACTACAAGAAAAACTCCTTGTGGTGAA

GGTTCTAAGACGTGGGATCGTTTCCAGATGAGAATTCACAAGCGACTCATTGAC

TTGCACAGTCCITCIGAGATTGTTAAGCAGATTACTTCCATCAGTATTGAGCCA

GGAGTTGAGGTGGAAGTCACCATTGCAGATGCTTAAGTCAACTATTITAATAAA

TTGATGACCAGTTGTTAAAAAAAAAAAAAAA

VI1-52

GAAAAGGGNTNGCNCOCAANGGGCAGAGGTTGGGCTGATGCCGATATTGGGCCN

CTGCNCTNCANACCTGGGTGACATGAATGAAACTCTGTCTCACATAAAAACCCA

AAAAANCTAAATGAAATAAAAGACCTTTGCTTATTNCTAANTTGGGTACGC

VII-15

CCCATCOCCTCGACCGCTCGOGTOGCATTTGGCOGCCTCCCTACCGCTCCAAGE

CCAGCCCTCAGCCATGGCATGCCCOCTGGATCAGGCCATTGGCCTCCTCRTGGE

CATCTTCCACAAGTACTCCGGCAGGGAGGGTGACAAGCACACCCTGAGCAAGAA

GGAGCTGAAGGAGCTGATCCAGAAGGAGCTCACCATTGGCTOGAAGCTGCAGGA

TGCTGAAATTGCAAGGCTGATGGAAGACTTGGACOGGAACAAGGACCAGGAGGT

GAACTTCCAGGAGTATGTCACCTTCCTGGGGGCCTTGGCTTTGAT

VII-32

AATTAGAGAGGTGAGGATCTGGTATTTCCIGGACTAAATTCCCCTTGGGGAAGA

CGAAGGGATGCTGCAGTTCCAAAAGAGAAGGACTCTTCCAGAGTCATCTACCTG

AGTCCCAAAGCTCCCTGTCCTGAAAGCCACAGACAATATGGTCCCAAATGACTG

ACTGCACCTTCTGTGCCTCAGCCGTTYTTGACATCAAGAATCTTCTGTTCCACA

TCCACACAGCCAATACAATTAGTCAAACCACTGTTATTAACAGATGTAGCAACA

TGAGAAACGCTTATGTTACAGGTTACATGAGAGCAATCATGTAAGTCTATATGA

CTTCAGAAATGTTAAAATAGACTAACCTCTAACAACAAATTAAAAGTGATTGTT

TCAAGGTGATGCAATTATTGATGACCTATTTTATT TTTCTATAATGATCATATA

TACCTTTGTAATAAAACATTATAACCAAAAAAAAAAAAAAAAAAAAAAAAASAA

AAAA

VI-48

CTTAAGTATGCCCT GACAGGAGATGAAGTAAAGAAGATTTGCATGCAGCGGTTC

ATTAAAATCOGATGGCAAGGTCCGAACT GATATAACCTACCCTGCTGGATTCATG

GATGTCATCAGCATTGACAAGACGGGAGAGAATTTCCGTCT GATCTATGACACC

AAGGGTOGCTTTGCT GTACATCGTATTACACCT! GAGGAGGCCAAGTACAAGTTG

TGCAAAGTGAGAAAGATCTTT GTGGGCACAAAAGGAATCCCT CATCTGGTGACT

CATGATGCCCGCACCATCCGCT. ACOCCGATCCCCTCATCAAGGTGAATGATACC

ATTCAGATTGATTTAGAGACTGGCAAGATTACT GATTTCATCAAGTTCGACACT

GGTAACCTGTGTATGGTGACT GGAGGTGCTAACCT AGGAAGAATTGGTGTGATC

ACCAACAGAGAGAGGCACCCTGGATCTTTT GACGTGGTTCACGTGAAAGATGCC

AATGGCAACAGCTTTGCCACT CGACTTTCCAACATTTTTGTTATIGGCAAGGGC

AACAAACCATGGATTTCTCT TCCCOGAGGAAAGGGTATCCGCCT CACCATTGCT

GAAGAGAGAGACAAAAGACT GGOGGCCAAACAGAGCAGTGGGTGAAATGGGTCC

CTGGGTGACATGTCAGATCTTTGT ACGTAATTAAAAATATTGTGGCAGGATTAA

TAGCC

VII-76

AGACACACGAGCATATTTCACCTCOGCT ACCATAATCATOGCTATCCCCACCGG

CGTCAAAGTATTTAGCTGACT CGCCACACTCCACGGAAGCAATATGAAAT GATC

TGCTGCAGTGCTCT GAGCCCTAGGATTCATCTT TCT TTTCACCGTAGGTGGCCT

GACTGGCATTGTATTAGCAAACT CATCACTAG ACATCGTACTACACGACACGTA

CTACGTTGTAGCCCACTTCCACT ATGTCCTATCAATAGGAGCTGTATTTGCCAT

CATAGGAGGCT TCATTCACTGATTTCCCCTATTCTCAGGCT ACACCCTAGACCA

AACCTACGCCAAAATCCATTTCACT ATCATATTCATCGGCOGTAAATCTAACTTT

CTTCCCACAACACTTTCTCGGCCT ATCCGGAATGCCCOGACGTTACTCGGACT A

CCCCGATGCATACACCACATGAAACATCCT ATCATCTGTAGGCT CATTCATTTC

TCTAACAGCAGTAATATTAATAATTTTCAT GAT TTGAGAAGCCTTCGCTT CGAA

GCGAAAAGTCCT AATAGTAGAAGAACCCT CCATAAACCTGGAGTGACTATATGG

ATGCCCCCCACCCT ACCACACATTCGAAGAACCCGTATACAT

© IX-24

AGAGTG CAAGACGATGACTTGCAAAATGTCGCAGCT GGAACGCAACATAGAGAC

CATCATCAACACCT TCCACCAATACTCTGTGAAGCT GGGGCACCCAGACACCCT

GAACCAGGGGGAATTCAAAGAGCT GGTGCGAAAAGATCTGCAAAATTTTCT CAA

GAAGGAGAAT AAGAATGAAAAGGTCAT AGAACACATCATGGAGGACCTGGACAC

AAATGCAGACAAGCAGCTGAGCTT OGAGGAGTTCATCATGCTGATGGCGAGGCT

AACCTGGGCCT COCACGAGAAGATGCACGAGGGTGACGAGGGCCCTGGCCACCA

CCATAAGCCAGGCCT CGGGGAGGGCACCCCCT. AAGACCACAGTGGCCAAGATCA

CAGTGGCCACGGCCACGGCCACAGTCATGETGGCCACGGCCACAGOCACT AATC

AGGAGGCCAGGCCACCCTGCCT CT ACCCAACCAGGGCCCCGGGGCCT GTTATGT

CAAACTGTCTTGGCT GTGGGGCTAGGGGCTGGGGCCAAATAAAGTCTCTT CCTC

CAAAAAAAA

IX-39

CTTGGCTCCTGTGGAGGCCT GCTGGGAACGGGACT TCTAAAAGGAACTATGTCT

GGAAGGCTGTGGTCCAAGG CCATTTTTGCTGGCT ATAAGCGGGGTCTCCGGAAC

CAAAGGGAGCACACAGCTCTT CTTAAAATTGAAGGTGTTTACGCCCGAGATGAA

ACAGAATTCT ATTTGGGCAAGAGATGCGCTT ATGTATATAAAGCAAAGAACAAC

ACAGTCACTCCT! GGCGGCAAACCAAACAAAACCAGAGTCATCTGGGGAAAAGTA

ACT (GGGCCCATGGAAACAGTGGCATGGTTCGTGCCAAATTCCGAAGCAATCT T

CCTGCT AAGGCCATTGGACACAGAATCCGAGTGATGCTGTACCCCT CAAGGATT

TAAACT AACGAAAAATCAATAAATAAATGTGGATTTGTGCT CTTGTA

1X46

ACGOGAGATGGCAGTGCAAAT ATCCAAGAAGAGGAAGTTTGTCGCT GATGGCAT

CIT CAAAGCTGAACTGAATGAGTTTCTTACT OGGGAGCTGGCTGAAGATGGCTA

CTCTGGAGTTGAGGTGOGAGTTACACCAACCAGGACAGAAATCATTATCT TAGC

CACCAGAACACAGAATGTTCT TGGTGAGAAGGGCCGGOGGATTOGGGAACTGAC

TGCIGTAGTTCAGAAGAGGTTTGGCTTTCCAGAGGGCAGTGTAGAGCTTE ATGC

TGAAAAGGTGGCCACTAGAGGTCT GTGTGOCATTGCCCAGGCAGAGTCTCTGCG

TTACAAACICCTAGGAGGGCTTGCT! GTGCGGAGGGCCTGCTATGGTGTGCTGCG

GTTCATCATGGAGAGTGGGGCCAAAGGCTGCGAGGTTIGTGGTGTCT GGGAAACT

CCGAGGACAGAGGGCT AAATCCATGAAGTTTGTGGATGGCCTGATGATCCACAG

CGGAGACCCTGTTAACT ACTACGTTGACACTGCT GTGCGCCACGTGTTGCTCAG

ACAGGGTGTGCTGGGCAT CAAGGTGAAGATCATGCTGCCCT GGGACCCAACTGG

TAAGATTGGCCCTAAGAAGCCCCTGCCT GACCACGTGAGCATTGTGGAACCCAA

AGATGAGATACTGCCCACCACCCCCATCT CAGAACAGAAGGGTGGGAAGCCAGA

© GCOGCCTGCCATGCCCCAGCCAGT CCCCACAGCATAACAGGGTCTCCTTGGCAG

CTGTATTCTGGAGTCTGGATGTTGCTCT CTAAAGACCTTTAATAAAATTTTGT

X-50

GTCCATCCTGCAGGCCACAAGCTCT GGATGAGGAACTTGAGGCAAGTCACCAGC

CCCTGATCATTT OGCCTAAAAGAGCAAGGACTAGAGTTCCT GACCTCCAGGCCA

GTCCCTGATCCCTGACCT. AATGTTATCGCGGAATGATGATATATGTATCT ACGG

CGGCCTGGGGCTGGGOGGACTCCIGCTTCTGGCAGTGGTCCTTCTGTCOGCCTG

CCTGTGTTGGCTGCATCGAAGAGTAAAGAGGCTGGAGAGGAGCTGOGCCCAGEE

CTCCICAGAGCAGGAACTCCACTATGCATCTCTGCAGAGGCTGCCAGTGCOCAS

CAGTGAGGGACCIGACCTCAGGGGCAGAGACAAGAGAGGCACCAAGGAGGATCC

AAGAGCTGACTATGCCTGCATTGCTGAGAACAAACCCACCTGAGCACCCCAGAC

ACCTTCCTCAACCCAGGCGGGTGGACAGGGTCCOCCTGTGGTCCAGOCAGTAAL

AAGCATGGTCCCCCCACTICTGTGTCTCAGTCCTCTCAGTCCATCICGAGCCTC

OGTT CAAAATGATCATCATCAAAACTTATGTGGCTTITTGACCTTTGAATAGGG

AATTTITTAAATITITTAAAAATTAAAATAAAAAAAACACATGGCTCACCCTTC

CACCCAAAAAAAAAA

X-77 (CTCCOGGGCTCTTAAGCCCCTCTCTTTCTCTAACAGAAAAAGOGGATGGTGGT

TCCTGCTGCCCTCAAGGTCGTGOGTCTGAAGCCTACAAGAAAGTTTGCCTATCT

GGGGCGCCTGGCTCACGAGGTTGGCTGGAAGTACCAGGCAGTGACAGCCACCLT

GGAGGAGAAGAGGAAAGAGAAAGCCAAGATCCACTACCGGAAGAAGAAACAGCT

CATGAGGCTACGGAAACAGGCOGAGAAGAACGTGGAGAAGAAAATTGACAAATA

CACAGAGGTCCTCAAGACCCACGGACTCCTGGTCTGAGCCCAATAAAGACTGTT

AATTCCTCATGOGTTGCCTGCCCTTCCTCCATTGTTGCCCTGGAATGTACGGGA

CCCAGGGGCAGCAGCAGTCCAGGTGCCACAGGCAGCCCTEGGACATAGGAAGCT

GGGAGCAAGGAAAGGGTCTTAGTCACTGOCTCCCGAAGTTGCTTGAAAGCACTC

GGAGAATTGTGCAGGTGTCATTTATCTATGACCAATAGGAAGAGCAACCAGTTA

CTATGAGTGAAAGGGAGCCAGAAGACTGATTGGAGGGCCCTATCTTGTGAGTGG

GGCATCTGTTGGACTTICCACCTGGTCATATACTCTGCAGCTGTTAGAATGTGC

AAGCACTTGGGGACAGCATGAGCTTGCTGTTGTACACAGGGTATT

X1-13 .

CTGCCAACATGGTGTTCAGGCGCTTCGTGGAGGTIGGCCGGGTGGCCTATGTCT

CCTTTGGACCTCATGCOGGAAAATTGGTCGCGATTGTAGATGTTATIGATCAGA

ACAGGGCTTTGGTCGATGGACCTTGCACTCAAGTGAGGAGACAGGCCATGCCTT

© CAAGTGCATGCAGCTCACTGATTTCATCCTCAAGTTTCCGCACAGTGCCCACC

AGAAGTATGTCCGACAAGCCTGGCAGAAGGCAGACATCAATACAAAATGGGCAG

CCACACGATGGGCCAAGAAGATTGAAGCCAGAGAAAGGAAAGCCAAGATGACAG

ATTTIGATCGTTTTAAAGTTATGAAGGCAAAGAAAATGAGGAACAGAATAATCA

AGAATGAAGTTAAGAAGCTTCAAAAGGCAGCTCTCCTGAAAGCTTCTCCCAAAA

AAGCACCTGGTACTAAGGGTACTGCTGCTGCTGCTGCTGCTGCTGCTGCTGCTG

CTGCTGCTGCTGCTGCTAAAGTTCCAGCAAAAAAGATCACCGCOGCGAGTAAAA

AGGCTCCAGCCCAGAAGGTTCCTGCCCAGAAAGCCACAGG CCAGAAAGCAGCGC

CTGOTCCAAAAGCTCAGAAGGGTCAAAAAGCTCCAGCCCAGAAAGCACCTGETC

CAAAGGCATCTGGCAAGAAAGCATAAGTGGCAATCATAAAAAGT AATARAGGTT

CTTTTTGACCTGTTAAAAAA

X1-49

GATCAACCTGGAGCT CTACGCCTCCT ACGTTTACCT GTCCATGTCTTACTACTT

TGACCGOGATGATGTGGCTTIGAAGAACTTTGCCAAATACTTTCTT CACCAATC

+ CATGAGGAGAGGGAACATGCTGAGAAACTGATGAAGCTGCAGAACCAACGAGG

TGGOCGAATCTTCCTTCAGGATATCAAGAAACCAGACTGTGATGACTGGGAGAG

CGGGCTGAATGCAATGGAGTGTGCATTACATTTGGAAAAAAATGTGAATCAGTC

ACT ACTGGAACTGCACAAACT GGCCACTGACAAAAATGACCCCCATTTGTGTGA

CTTCATTGAGACACATTACCTGAATGAGCAGGTGAAAGCCATCAAAGAATTGGG

TG ACCACGTGACCAACTTGCGCAAGATGGGAGCGCCCGAATCTGOCTTGGCGGA

ATATCTCTTTGACAAGCACACCCT GGGAGACAGTGATAATGAAAGCT AAGCCTC

GGGCTAATTTCCCCATAGCCGTGGGGTGACT TCCCT GGTCACCAAGGCAGTGCA

TGCATGTTGGGGTTTCCITTACCTTTTCTATAAGTTGTACCAAAACATCCACTT

AAGTTCTTTGATTTGTACCATTCCTTCAAATAAAGAAATTTGGTACCC

X1-81

AGAGCAGCAGCCATGGCCCT ACGCTACCCT ATGGCCGTGGGCCT CAACAAGGGC

CACAAAGTGACCAAGAACGTGAGCAAGCCCAGGCACAGOOGACGCCGOGGGOGT

CTGACCAAACACACCAAGTTCGTGCGGGACATGATTCGGGAGGTGTGTGGCTTT

GCCCOGTACGAGCGGOGCGCCATGGAGTTACTGAAGGTCTCCAAGGACAAACGG

GCCCTCAAATTTATCAAGAAAAGGGTGGGGACGCACATCCGOGCCAAGAGGAAG

CGGGAGGAGCTGAGCAACGTACTGGCCGCCATGAGGAAAGCTGCTGCCAAGAAA

GACTGAGCCCCTCCCCTGCOCTCTCCCTGAAATAAA

X11-35

CTCTCCTGTCAACAGCGGCCAGOCTCCCAACTACGAGATGCTCAAGGAGGAGCA

GGAAGTGGCTATGCTGGGGGCGOCCCACAACCCTGCTCOCCCGACGTCCACCST

GATCCACATCCGCAGCGAGACCTCOGTGCCOGACCATGTCGTCTGGTCCCTGTT

CAACACCCICTT CATGAACACCTGCT GCCTGGGCTT CATAGCATTCGCCTACTC

CGTGAAGTCTAGGGACAGGAAGATGGTTGGCGACGTGACCGGGGCCCAGGCCTA

[GCCTCCACCGCCAAGTGCCTGAACATCTGGGCCCTGATTTTGGGCATCTTCAT

GACCATTCT! GCTCGTCATCATCCCAGTGTTGGTCGT CCAGGCCCAGCGATAGAT

CAGGAGGCATCATTGAGGCCAGGAGCTCTGCCCGTGACCTGTATCCCACGTACT

CTATCTTCCATTCCT CGCOCTGCCCCCAGAGGCCAGGAGCT CTGCCCTTGACCT

GTATTCCACTTACT CCACCTTCCATTCCTCGCCCT! GTCCCCACAGCCGAGTCCT

GCATCAGCCCTTTATCCTCACACGCTT TTCTACAATGGCATTCAATAAAGTGTA

TATGTTTCTGGTGCTGCTGTGACIT CAA

X11-77

GTAAGAAAGCCCTT AAATAAAGAAGGTAAGAAACCT AGGACCAAAGCACCCAAG

ATTCAGCGTCTTGTTACT CCACGTGTCCTGCAGCACAAACGGCGGCGTATTGCT

CTGAAGAAGCAGCGTACCAAGAAAAATAAAGAAGAGGCT GCAGAATATGCTAAA

CTTTTGGCCAAGAGAATGAAGGAGGCT AAGGAGAAGCGCCAGGAACAAATTGCG

AAGAGACGCAGACTTTCCTCTCTGOGAGCTT CTACTTCTAAGTCTGAATCCAGT

X1-29

CTCGCTCACGCAGCACTOGTGGCAGTCCCT GAAGGACCGCTACCTCAAGCACCT

GOGGGGCCAGGAGCATAAGTACCTIGCT GGGGGACGCGCCGGTGAGCCCCTCCTC

CCAGAAGCTCAAGCGGAAGGCGGAGGAGGACCOGGAGGCCGOGGATAGCGGGGA

ACCACAGAATAAGAGAACTCCAGATTTGCCT GAAGAAGAGTATGTGAAGGAAGA

AATCCAGGAGAATGAAGAAGCAGTCAAAAAGATGCTTGTGGAAGCCACCCGGGA

GTTTGAGGAGGTTGTGGTGGATGAGAGCCCTCCT GATTTTGAAATACATATAAC

TATGTGTGAT GATGATCCACCCACACCTGAGGAAGACT CAGAAACACAGCCTGA

TGAGGAGGAAGAAGAAGAAGAAGAAAAAGTTTCT CAACCAGAGGTGGGAGCTGC

CATTAAGATCATTCGGCAGTTAATGGAGAAGTTTAACT TGGATCTATCAACAGT

TACACAGGCCITCCY. AAAAAATAGTGGTGAGCTGGAGGCTACTT COGCCITCTT

AGCGTCTGGTCAGAGAGCTGATGGATATCCCATT TGGTCCCGACAAGATGACAT

AG ATTTGCAAAAAGATGATGAGGATACCAGAGAGG CATTGGTCAAAAAATTTGG

TGCTCAGAATGTAGCT CGGAGGATTGAATTTCGAAAGAAATAATTGGCAAGATA

ATGAGAAAAGAAAAAAGTCATGGTAGGTGAGGTGGTTAAAAAAAATTGTGACCA

ATGAACTTTAGAGAGTTCTT! GCATTGGAACTGGCACTTATTTTCTGACCATCGC © TGCTGTTGCTCIGTGAGTCCTAGATT

X1n-84

ATTATCCTCAGTTCCCAAGAGCAATCATACTTT TCCACACATACOGTGTGTCTC

ATGTTAGGTAAATGTATTTTTACAATGAGCACCACTTCT GTGGAAAAAGTTCCC

TGCACGGGGAG GTCCAGCTTCCAGACTGCTCCATCGCATAAGGACT TCCCCATT

CCCCTAAATGCTGCTCTGTCAGAACCT GCCCAGGTAATGGTAATGACCCTAGAG

AGATGATTTCT GAACCGCAATTTTGAGCCCATTAGAAGGTGTGTGGTGGGCATT

TATTTCATCCTGATGCTCT GGTGAGAATCTTTGCAGACGCACT AGATCCAGAAG

CTGTTAATCTTGGTGCATTTATTTTCCT ACCTAAAAGAACCAAGCAGCTCAGAG

GCAGTGACTGTACAGGATGCAGTGTTTATAATAATGCT GAGCTTGCTGGTCIGG

AACCCCACACTT CAGCAATCCCAGCATTGTTCCTGTTTATGAAGTTGACAAAGT

GACCAGGGCAAGGGGGT ATTATCATTAAATACACTCT AGGAGAGGCAGAACACA

TGAGGGCAATGTTTTTCAGAGGTCTTTAGGCCACCGCATCAGATICT CCTGGAG

CATAAAGCAAATGCTT TATGAGTCCAGGGCCCCT GCAGACCTACTGTATACTAG

TATACAGCTCCCTCTTAGTGGATCT CAAGCTTGTTTCCAAAAAGTCATTACACT

COTTACCAAAGCCCATGACACATTCATACAGATTCAT CCAGACATAACCCACTG

CATGGTCCAGTGCATGCTTGTGTGCTT AACTTATTATAGATCAAGTGTTATTTA

AGTCCAACATATTAAACGTGACT GAATATT

XV-49

AAGTCTGCCCAGAAAGCTCAGAAGGCT AAATGAATATTATCCCTAATACCTGCC

ACCCCACTCTT AATCAGTGGTGGAAGAACGGTCT CAGAACTGTTTGTTITCAATT

GGCCATTTAAGTTTAGTAGTAAAAGACTGGTTAATGATAACAATGCATCGTAAA :

ACCT TCAGAAGGAAAGGAGAATGTTTTGTGGACCACTTT GGTTTTCTTTTTTGC

GTGTGGCAGTTTTAAGTTATTAGTT TTTAAAATCAGTACTTTTTAATGGAAACA

ACTT GACCAAAAATTTGTCACAGAATTTTGAGACCCATTAAAAAAGTTAAATGAG

XV-54

AAGAGCAGGTCTCTGGAGGCT! GAGTTGCATGGGGCCTAGTAACACCAAGCCAGT

GAGCCTCTAATGCTACTGCGCCCTGGGGGCT CCCAGGGCCTGGGCAACTTAGCT

GCAACT GGCAAAGGAGAAGGGTAGTTTGAGGTGTGACACCAGTTTGCTCCAGAA

AGTTTAAGGGGTCIGTTTCTCATCT CCATGGACATCTTCAACAGCTTCACCTGA

CAACGACTGTTCCTATGAAGAAGCCACT TGTGTTTTAAGCAGAGGCAACCTCTIC

TCTTCT CCTCTGTITCGTGAAGGCAGGGGACACAGATGGGAGAGATTGAGCCAA

GTCAGCCTTCIGTT GGTTAATATGGTATAATGCATGGCTTT GTGCACAGCCCAG

TGTGGGATTACAGCTTTGGGATGACCGCT TACAAAGTTCIGTTTGGTTAGTATT

} GGCATAGTTTTTCTATATAGCCATAAATGOGTATATATACCCATAGGGCTAGAT

CT GTATCTTAGTGTAGCGATGTATACATATACACATCCACCT ACATGTTGAAGG

GCCTAACCAGCCTTGGGAGTATTGACT GGTCCCTTACCTCTTATGGCTAAGTCT

TTGACTGTGTTCAT TTACCAAGTTGACCCAGTTTGTCTIT TAGGTTAAGTAAGA

CTCGAGAGTAAAGGCAAGGAGGGGGGCCAGCCT CTGAATGCGGCCACGGATGCC

TTGCTGCTGCAACCCTTTCCCCAGCTGTCCACT GAAACGTGAAGTCCTGTTTTG

AATGCCAAACCCACCATTCACTGGTGCTGACT ACATAGAATGGGGTTGAGAGAA

GATCAGTTTGGGCTTCACAGTGTCATTTGAAAACGTTTTTIGTTTTGTTTTGTA

ATTATTGTGGAAAACTTICAAGTGAACAGAAGGATGGTGTCCTACTGTGGATGA

GGGATGAACAAGGGGATGGCTTIGATCCAATOGAGCCTGGGAGGTGTGCCCAGA

AAGCTTGTCTGTAGCGGGTTTTGTGAGAGTGAACACITTCCACT TT TIGACACC

TTATCCTGATGTATGGTTCCAGGATTTGGATTT TGATTTTCCAAATGTAGCTTG

AAATTICAATAAACTTTGCTCTGTTTTTCTAAAAATAAAAAAAAAAAAAAAAAA

AAAAAAAA

XV-75

A GCAGATGACCCTTCGTGGCACCCTCAAGGGCCACAACGGCTGGGTAACCCAGA

TOGCTACTACCCCGCAGTTCCCGGACATGATCCTCTCOGCCTCTCGAGATAAGA

COATCATCATGTGGAAACTGACCAGGGATGAGACCAACTATGGAATTCCACAGC

GTGCTCTGOGGGGTCACTCCCACTTTGTTAGTGATGTGGTTATCTCCTCAGATG

GOCAGTTIGOCCTCTCAGGCTCCTGGGATGGAACCCTGOGCCTCTGGGATCTCA

CAACGGGCACCACCACGAGGCGATTTGTGGGCCATACCAAGGATGTGCTGAGTG

TGGCCTTCTOCTCTGACAACCGGCAGATTGTCTCTGGATCTCGAGATAAAACCA

TCAAGCTATGGAATACCCTGGGTGTGTGCAAATACACTGTCCAGGATGAGAGCC

ACTCAGAGTGGGTGTCITGTGTCOGCTTCTCGCCCAACAGCAGCAACCCTATCA :

TCGTCTCCTGTGGCTGGGACAAGCTGGTCAAGGTATGGAACCTGGCTAACTGCA

AGCTGAAGACCAACCACATIGGOCACACAGGCTATCTGAACACGGTGACTGTCT

CTCCAGATGGATCOCTCTGTGCTTCTGGAGGCAAGGATGGCCAGGCCATGTTAT

GGGATCTCAACGAAGGCAAACACCTTTACACGCTAGATGGTGGGGACATCATCA

ACGCCCIGTGCTTCAGCCCTAACOGCTACTGGCTGTGTGCTGCCACAGGOCCCA

GCATCAAGATCTGGGATTTAGAGGGAAAGATCATTGTAGATGAACTGAAGCAAG

AAGTTATCAGTACCAGCAGCAAGGCAGAACCACCCCAGTGCACCTCCCTGGCCT

GGTCTGCTGATGGCCAGACTCTGTTTGCTGGCTACACGGACAACCTGGTGCGAG

TGTGGCAGGTGACCATTGGCACACGCTAGAAGTTTATGGCAGAGCTTTACAAAT

AAAAAAAAAACTGGCTTTTCTGACAAAAAAAAAA

XV-86

GCAAAATGTCGCAGCTGGAACGCAACATAGAGACCATCATCAACACCTTCCACC

‘ AATACTCTGTGAAGCTGGGGCACCCAGACACCCTGAACCAGGGGGAATTCAAAG

AGCTGGTGOGAAAAGATCTGCAAAATTTTCTCAAGAAGGAGAATAAGAATGAAA

AGGTCATAGAACACATCATGGAGGACCTGGACACAAATGCAGACAAGCAGCTGA

GCTTCGAGGAGTTCATCATGCTGATGGCGAGGCTAACCTGGGCCTCCCACGAGA

AGATGCACGAGGGTGACGAGGGCCCTGGCCACCACCATAAGCCAGGCCTOGGGE

AGGGCACCOCCTAAGACCACAGTGGCCAAGATCACAGTGGCCACGGCCACGGCE

ACAGTCATGGTGGCCACGGCCACAGCCACT AATCAGGAGGCCAGGCCACCCT GCCT

CT ACCCAACCAGGGCCCCGGGGCCTGTTATGTCAAACTGTCTTGGCT GTGGG

GCTAGGGGCT GGGGCCAAATAAAGTCTCTT CCT CCAAAAAAAAAAAAAAAAAAA

AAAAAAAAAAAAAAAAAAAAAA

XV1-74

CG CCGCCGOGCOGCCGTCGCT CTCCAACGCCAGCGCOGCCICT COGCTCGCCGAG

CT CCAGCCGAAGGAGAAGGGGGGTAAGTAAGGAGGT CTCTGTACCATGGCTCGT

ACAAAGCAGACT! GCCCGCAAATCGACCGGTGGTAAAGCACCCAGGAAGCAACT G

GCTACAAAAGCOGCT CGCAAGAGTGCGCCCT CTACTGGAGGGGTGAAGAAACCT

CATCGTTACAGGCCTGGTACT GTGGCGCT! COGTGAAATTAGACGTTATCAGAAG

TCCACTGAACT TCTGATTCGCAAACTTCCCTTCCAG CGTCTGGTGCGAGAAATT

GCTCAGGACTTTAAAACAGATCT GOGCTTCCAGAGCGCAGCT ATCGGTGCTTITG

CAGGAGGCAAGTGAGGCCTATCT GGTTGGCCT TTTTGAAGACACCAACCTGTGT

GCTAT CCATGCCAAACGTGTAACAATTATGCCAAAAGACATCCAGCT AGCACGC

CGCATACGTGGAGAACGTGCTT. AAGAATCCACT ATGATGGGAAACATTTCATTC

TCAAAAAAAAAAAAAAAAAATTTCTCTT CTTCCTGTTATTGGTAGTTCIGAACG

TTAGATATTTTTTTTCCATGGGGTCAAAAGGTACCT AAGTATATGATTGCGAGT

GGAAAAATAGGGGACAGAAATCAGGTATTGGCAGTTITTCCATITTICATTTGTG

TGTGAATTTTTAATATAAATGCGGAGACGTAAAGCATTAATGCAAGTTAAAATG

TTTCAGTGAACAAGTTTCAGCGGTTCAACTT TATAATAATTATAAATAAACCTG

TTAAATTTTTCT GGACAATGOCAGCATTTGGATTTTTTTAAAACAAGTAAATTT

CTTATTGATGGCAACT AAATGGTGTTTGTAGCATTTTTATCATACAGTAGATTC

CATCCATTCACTATACTTTTCTAACT GAGTTGTCCTACATGCAAGTACATGTTT

TTAATGTTGTCTGTCTTCTGTGCTGTTCCT GTAAGTTTGCTATTAAAATACATT

AAACTATAAAAAAAAAAAAAAAAAAA

XVII-77

CAGACACCCT GAACCAGGGGGAATTCAAAGAGCT GGTGCGAAAAGATCTGCAAA

ATTTTCT CAAGAAGGAGAATAAGAATGAAAAGGTCATAGAACACATCATGGAGG

ACCTGGACACAAATGCAGACAAGCAGCT GAGCTTCGAGGAGTTCATCATGCTGA

TGGCGAGGCTAACCT GGGCCTCCCACGAGAAGATGCACGAGGGTGACGAGGGCC

CTGGCCACCACCATAAGCCAGGCCT CGGGGAGGGCACCCCCTAAGACCACAGTG

GCCAAGATCACAGTGGCCACGGCCACGG CCACAGTCATGGTGGCCACGGCCACA

GCCACTAATCAGGAGGCCAGGCCACCCT GCCTCTACCCAACCAGGGCCCCGGGG

CCTGTTATGTCAAACTGTCTTGGCT GTGGGGCTAGGGGCTGGGGCCAAATAAAG

TCTCTTCCTCCAAAAAAA

XII-78 no sequence available

Table 1. Sample detail. Stage 0, in situ carcinoma; Stage I, invasive carcinoma with tumour size < 20 mm; Stage II, invasive carcinoma with tumour size > 20-50 mm; Stage III, invasive carcinoma with tumour size >50 mm. Stage IV, cancer spread to distant parts. IDC, invasive ductal carcinoma, DCIS, ductal carcinoma in situ; ILC, invasive lobular carcinoma. n.a.,-not available. ND, non-decision. +, Blood samples taken at five consecutive weeks from the same female 2 PY IPO Pp I == EE

ID Age | Suge Histology Grade Size (mny) Nodes presen/comments assayed | prediction

EE eee we : | 8 we am | we [ola [

SEN Er RS I ITTV AN I EE EE

Be Hi fn rs MEL eT we ew oo [2] ND

Sr Era—— EE ETE - 2 | aw e Tso lu] we [2 | 0 - + 9 IE NE ATR NN I I i AY No fr HY EW EP BR + bupre and linfraclavicular, 11 65 Iv nodes -

I + is rir me — Ts [eo | - [a =~

Tw lo | wu | o |." lz | + te Termes ew [0 1s +

STEEP IY ES A ENS NN A : 17 7 IE = NY AE RW EN EE + wml we | a | ue | 0 | >

Te we [we Ie [| +

EE i sis + 2 | 4 Tw la ws | o [| Tt] + te Tw we ow | oe Lb +

Ta we oe oe 1 [] +

Subgroup A2: Women with abnormal first mammography

Other disease if Times rami re [a | pepe | oa [x

ET Er RE Rr EE

3 [| eimmminions | Spettciom initia | 1 + micas | Coon pinion | 1 | 0

Eo IT IE reer IP NS EEE 3 SF rere I RE RS 31 BS Ere A ECU Ra mem | Geetimon | 3 |W eT hem | pepe | 5

OT Somme | | 2 | + 3 A mimi] Cowes | 3 | +

SEI PS TR vee ER EE

SN rT Yeeros ES RT RS

SE I ETT ES RN NEE aT ree swmepeoms | 1 Lt

Te begaesy | pupwes | 2 | + 2 Benign density I NY

ET mew | wren | 1] +

Subgroup A3: Women with no breast abnormality a] oe [

ID Age Comments assayed | Predictiom w || - | 2 | +

Hi-a 45 34 months 3 +

SLE

27 months + os | wed 2 | + wed] 1 | +] wed 1 [+ |]

Weel] 1 | +

Lm 48 months

Tag | 30 | Btpeding | 2 | +

ENE I EET

EE AE Bra

Ea m——— 3 ENE I [54 | 34 | Brestfting | 3 | + s 1-1 [1 + infection in udddition to chronic EBV 56 51 infection 1 + i 71

Table 2 Details of the 35 significant genes selected by Jackknife. Their position in the amay, clone ID is shown as well as the accession number of sequences in public databases that match them, and their known or putative cellular function.

UPREGULATED GENES

Position ID oo] omen | Putative biological function 6A ET I NE

Chromosome 3 clone RP11- 321A23; From sequence no. nr-27 10M AC096970 135183-135446

Signal transduction, Kinase

Ras homolog geas family, member inhibitor? (RboH has been 1-60 14AC NM_001665 te G (rho G) described as a kinase inhibétor)

Iron storage; defence against

IV-26 BC016857 Ferritin, heavy polypeptide 1 ROS

Upstream transcription factor 2, .

USF2 iv-51 102 BC042655 — * Transciptional regulator

Chromosome 11, From sequence

VI-44 14X AC087441 no. 116068-116692 on as wee scien $100 calcium binding protein A6

BC001431 (calcyclin) Defence; inhibition of caseine vis | om kinase II

Human low-affinity IgG Fe

ViI-32 31M M28697 receptor (alpha- Fo-gamma-RII) Immune response

S100 calcium binding protein A9 | Defence; inhibition of caseine

X-24 31) BC047681 (calgranulin B) kinase II _IX:50 72 momar |] Defeace-related

Ferritin, heavy polypeptide 1, Iron storage; defence against

XI-49 3AB BC016857 mRNA ROS

Interferon induced transmembrane

X-35 12Q BC0096%6 protein 2 Immune response

ETE EE ES A RA

From sequence number 75875-

X11-84 16AP A1391903 76710

Delta sleep inducing peptide,

XV-54 24AA BC018148 immunoreactol Immune response?

S100 calcium binding protein A9 | Defence; inhibition of oaseine

XV-86 24AQ BC047681 (calgranulin B) kinase II

Xvi74 | sak HS histone, family 38 (3.38)

S100 calcium binding protein A9 | Defence; inhibition of caseine

KVI-77 20AN BC047681 (calgranulin B kinase II

Table 2 -«~™* } Downregulated genes

Clone | Position | Accession Putative biolog/ cal

ID ID number Gene similarity function

Cyclin D-type binding E2F-mediated i-30 210 | BC00%683 protein transcription

Ribosomal protein

WV-41 2V BC010165 S2 Ribosome production

V-09 | 2G | BC053370 Ribosomal protein SA Ribosome production

Ribosomal protein .

V-38 22S | NM_001018 S12 Ribosome production

Ribosomal protein

VI-49 2AB |NM_001023 $20 (RPS20)

Vikas] 31U_| M22146 [Ribosomal protein S4jRibosome production

Cytochrome C oxidase subunit, COX Mitochondrial electron

Vil-76 | 15AK | AY495316 1 transport chain

Ribosomal protein

IX-39 | 27R BC001037 L35a Ribosome production

Ribosomal protein 1X-46 23V BC034149 S3 Ribosome production

Ribosomal protein 419AM [| BCO000514 L13a Ribosome production

Ribosomal protein

D87735 L14 Ribosome production 60S Ribosomal

X|-81 3AR AF077043 protein L36 Ribosome production

Ribosomal protein

Xil-77| 20AK | BC035447 S6 Ribosome production

Telomeric repeat binding factor 2, Telomere length

Xli-29| 20N BC004465 | interacting protein regulation

Eukaryotic translation elongation factor 1 alpha 1,

XV-49] 4AA BCO018641 (EEF1A) Protein translation

Guanine nucleotide binding protein, beta polypeptide 2-like ;

RACKSs (for 'recepto for activated C-

XV-75] 12AM | BC019093 kinase) Protein translation

Hp

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Claims (48)

1. A method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) isolating mRNA from the cells of a sample of one or more organisms having the cancer or stage thereof, which may optionally be reverse transcribed: to cDNA, b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotide probes specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation, wherein said set comprises at least 10 oligonucleotides, wherein each oligonucleotide is selected from an oligonucleotide corresponding to a gene sequence from: family (i) genes encoding proteins involved in protein synthesis and/or stability; or family (ii) genes encoding proteins involved in the regulation of defence and/or chromatin remodelling; or derived from such a sequence, or an oligonucleotide with a complementary sequence, or a functionally equivalent oligonucleotide; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in the sample with the cancer or stage thereof.

2. A method of preparing a test gene transcript pattern comprising at least the steps of: a) isolating mRNA from the cells of a sample of said test organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to aset of oligonucleotides as defined in claim 1 specific for a cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and

¢) assessing the amount of mRNA or cDNA hybridizing to each of sald probes to produce said pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said test sample.

3. A method of diagnosing or identifying or monitoring a cancer or stagc thercof in an organism, comprising the steps of: a) isolating mRNA from the cells of a sample of said organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a sct of oligonucleotides as defined in claim 1 specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thercof under investigation; c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said sample; and d) comparing said pattern to a standard diagnostic pattern preparcd according to the method of claim 1 using a sample from an organism corresponding to the organism and sample under investigation to determine the presence of said cancer or a stage thereof in the organism under investigation.

4. A method as claimed in any one of claims 1 to 3 wherein said family (1) genes encoding proteins involved in protein synthesis and/or stability are selected from the group consisting of: (a) genes encoding ribosomal proteins and ribosomal activation proteins; (b) genes encoding translation inhibition and initiation factors; and (c) genes encoding other modulators of transcription or translation.

5. A method as claimed in claim 4 wherein said ribosomal proteins and ribosomal activation proteins are selected from the group consisting of ribosomal proteins L1-L56, L7A, L10A, L13A, L18A, L23A, L27A, L35A, L36A, L37A, PO, P1, P2, S2-§29, S31, §33-S836, S3A, S15A, S18A, S18B, S18C, S27A, 63, 115 (and pseudogenes), ribosomal protein kinases, ribonucleases, putative S1 RNA binding domain protein, eukaryotic translation initiation factors and guanine nucleotide binding protein G. Amended sheet: 9" June 2008

0. A method as claimed in claim 4 or 5 wherein said translation inhibition and initiation factors are selected from the group consisting of eukaryotic translation elongation factors, tRNA synthetases, RNA binding proteins, polyadenylation element binding proteins, tyrosine phosphatases, eukaryotic translation initiation factors, and RNA polymerase I, III transcription factors.

7. A method as claimed in any one of claims 4 to 6 wherein said other modulators of transcription or translation are sclected from cyclin D-type binding protein and guanine nucleotide binding protein.

8. A method as claimed in any onc of claims 1 to 7 wherein said family (i1) genes encoding proteins involved in the regulation of defence and/or chromatin remodelling family are selected from the group consisting of: (a) genes encoding immune response related proteins; (b) genes encoding TNF-induced proteins; (c) genes encoding hypoxia-induced proteins; (d) genes encoding oxidative stress proteins; and (e) genes encoding proteins involved in chromatin remodelling.

9. A method as claimed in claim 8 wherein said immune response related proteins are selected from the group consisting of T-cell receptor and associated components, various cytokines, interferon regulatory factors, oncostatin M, Leukemia inhibitory factor, chemokine ligand and receptor family, complement components, interferon stimulated factors, MHC class or II (or related components), adhesion proteins, nuclear factor of kappa polypeptide gene enhancer in B-cells, myelin basic protein, cathepsin, toll- like receptor, protcosome subunits, ferritin, protein kinases or phosphatases as well as their activators and inhibitors, leukocyte immunoglobulin-like receptor, immunoglobulin components, defensin, oxytocin, S100 calcium binding protein, lectin and its receptor and superfamily, leptin, phospholipase and growth factors.

10. A method as claimed in claim 8 or 9 wherein said TNF-induced proteins arc selected from the group consisting of TNF alpha-induced protein Amended sheet: 9" June 2008

8, integrin, inhibitor of kappa light polypeptide gene enhancer in B-cells, TNF-associated factor 2, 5, nuclear factor of kappa light polypeptide gene cnhancer in B-cells, MAP kinases, protein kinase C, ubiquitous kinase, cadherin, caspase, cyclin D1, superoxide dismutase and interleukins.

11. A method as claimed in any one of claims 8 to 10 wherein said hypoxia-induced proteins are sclected from the group consisting of sestrin, E1A binding protein p300, endothelin, ataxia telangiectasia and Rad3 related protein, hexokinase 2, TEK tyrosine kinase, DNA fragmentation factor, caspase, plasminogen activator, hypoxia-inducible factor 1 and glucose phosphate isomerase.

12. A method as claimed in any one of claims 8 to 11 wherein said oxidative stress proteins arc selected from the group consisting of superoxide dismutase, glutathione synthetase, catalase, lactopcroxidase, thyroid peroxidase, myeloperoxidase, eosinophil peroxidase, oxidation resistance 1, peroxiredoxin, cytochrome P450, scavenger receptor, paraoxonase, glutathione reductase, NAD(P)H dehydrogenase, glutathione S-transferase, catenin, glutaredoxin, heat shock proteins, mitogen-activated protein kinases, enolase, thioredoxin reductase and peroxiredoxin.

13. A method as claimed in any one of claims 8 to 12 wherein said proteins involved in chromatin remodelling are histone replacement proteins.

14. A mcthod as claimed in any one of claims 8 — 13, wherein: (1) said cytokines arc the interleukins or their receptors or tumour necrosis factor or its receptor or its superfamily; and/or (11) said adhesion proteins are CD1A, CDI1C, CDI1D, CD3Z, 6,8, 11, 14, 18, 24,27, 28, 29, 40, 44, 50, 54, 59, 74, 79B, 80, 81, 83, 86, 96 or ICAM; and/or (in) said immunoglobulin components are heavy chain or Fc fragments; and/or (iv) said growth factors are endothelial cell growth factor or erythropoictin. Amended sheet: 9" June 2008

1S. A method as claimed in claim 14 wherein said interleukins are selected from the group consisting of IL-1, 2, 3,4, 5,6, 7, 8,9, 10, 11, 12, 13, 15,17, 18, 20,22 and 24.

16. A method as claimed in claim 14 or 15 wherein said tumour necrosis factor superfamily is selected from the group consisting of TNF superfamily members 2,3,4,5,6,7,8,9,11,12,13, 14 and 15.

17. A method as claimed in any one of claims 14 to 16 wherein said Fc fragments are sclected from IgG, IgE or IgA or their superfamily.

18. A method as claimed in any one of claims 1 to 17 wherein said immunc response proteins encoded by family (ii) genes are adhesion proteins, interleukins, their receptors or superfamily, TNT its receptor or superfamily, immunoglobulin components or erythropoietin.

19. A method as claimed in any one of claims 1 to 18 wherein the genes encoding the (i) families are down-regulated in cancer versus normal patients and in the case of (ii) families the encoding gencs are up-regulated.

20. A method as claimed in any one of claims 1 to 19 wherein said probes correspond to genes which are systemically affected by said cancer or stage thereof.

21. A method as claimed in any onc of claims 1 to 20 wherein said genes are constitutively moderately or highly expressed.

22. A method as claimed in any one of claims 1 to 20 wherein said set comprises a combination of oligonucleotides from family (i) and family (ii).

23. A method as claimed in any onc of claims 4 to 22 wherein said set of oligonucleotides includes oligonucleotides from family (i)a, (i1)a and (ii)e. Amended sheet: 9" June 2008

24. A method as claimed in any one of claims 1 to 23 wherein said set includes oligonucleotides from genes encoding one or more ribosomal proteins and optionally one or more histones and optionally ferritin.

25. A method as claimed in any one of claims 1 to 24 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2 or 3, or derived from a sequence described in Table 2 or 3, or a complementary scquence thereof.

26. A method as claimed in claim 25 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2, or derived from a scquence described in Table 2, or a complementary sequence thercof.

27. A method as claimed in claim 25 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 3, or derived from a scquence described in Table 3, or a complementary sequence thereof.

28. A method as claimed in any one of claims 25 to 27 wherein said set additionally comprises one or more oligonucleotide probes selected from the oligonucleotides listed in Table 4, or derived from a scquence described in Table 4, or a complementary sequence thereof.

29. A mcthod as claimed in any one of claims 25 to 28 wherein said derived oligonucleotide of Table 2, 3 or 4 is a part of a gene described by its Accession number in Table 2, 5 or 6, respectively, or a complementary sequence thereof.

30. A method as claimed in any one of claims 1 to 29 wherein said set consists of from 10 to 500 probes.

31. A method as claimed in any one of claims 1 to 30 wherein said set of probcs are immobilized on one or more solid supports. Amended sheet: 9" Junc 2008

32. A method as claimed in any one of claims 1 to 31 wherein said cells are not disease cells, have not contacted disease cells and do not originate from the site of disease.

33. A method as claimed in any one of claims 1 to 32 wherein said sample has been obtained from a site distant to the site of disease.

34. A method as claimed in any one of claims 1 to 33 wherein said samplc 1s tissue, body fluid or body waste.

35. A method as claimed in claim 34 wherein said sample is peripheral blood.

36. A method as claimed in any onc of claims 1 to 35 wherein said cancer is stomach, lung, breast, prostate gland, bowel, skin, colon or ovary cancer.

37. A method as claimed in claim 35 wherein said cancer in breast cancer.

38. A method as claimed in any one of claims 1 to 37 wherein said organism is a mammal.

39. A method as claimed in claim 38 wherein said mammal is human.

40. A method as claimed in any one of claims 1 to 39 wherein at least one of said probes in said set is suitable for diagnosing, identifying or monitoring at lcast two of said cancers or stages thereof.

41. A method of diagnosis or identification or monitoring as claimed in any one of claims 3 to 40 for the diagnosis, identification or monitoring of two or more cancers or stages thereof in an organism, wherein said test pattern produced in step c) of the diagnostic method is compared in step d) to at least two standard diagnostic patterns prepared as defined in any onc of claims 1 or 4 to 40, wherein each standard diagnostic pattern is a pattern gencrated for a different cancer or stage thereof. Amended sheet: 9™ June 2008

42. A set of less than 500 oligonucleotide probes as defined in any one of claims 1 to 41.

43. Ait for performing a method as claimed in any one of claims 1 to 41 comprising a set of oligonucleotide probes as defined in claim 42 immobilized on one or more solid supports.

44. A kit as claimed in claim 43 additionally comprising a package insert detailing how the method should be performed.

45. The use of a sct of oligonucleotide probes or a kit as defined in any one of claims 42 to 44 to determine the genc expression pattern of a cell which pattern reflects the level of gene expression of genes to which said oligonucleotide probes bind, comprising at least the steps of: a) isolating mRNA from said cell, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotide probes or a kit as defined in any one of claims 42 to 44; and ¢) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern.

46. A method as claimed in claim 1 substantially as herein described with reference to cither example 1 or 2.

47. A method as claimed in claim 2 substantially as herein described with reference to cither example 1 or 2.

48. A method as claimed in claim 3 substantially as herein described with reference to either example 1 or 2. Amended sheet: 9" June 2008